Forem: Fabio Augusto Suizu

Add Speech to Your AI Agent in 5 Minutes

Fabio Augusto Suizu — Thu, 26 Feb 2026 01:00:28 +0000

Your agent reads text. It writes text. It reasons about text. But it can't hear a student say "hello" and tell them their /h/ is perfect while their /l/ needs work. Here's how to fix that.

The Problem

AI agents are text-native. They consume text, produce text, and reason over text. But a growing class of applications requires agents to interact with the physical world through sound:

Language tutoring agents that listen to a student and give phoneme-level feedback
Customer service agents that transcribe calls and respond with natural speech
Accessibility agents that read content aloud and accept voice commands
Interview practice agents that evaluate spoken responses

You could try to hack this with LLM inference. Ask Opus to "evaluate this pronunciation" and you'll get a confident, plausible, and completely fabricated phoneme analysis. LLMs don't have acoustic models. They can't compute phoneme-level pronunciation scores because they never see the audio signal.

What you need are specialized speech tools.

The Solution: Speech AI Tools

We ship three speech APIs as both a REST interface and an MCP server with 8 tools:

Capability	Tool	What It Does	Latency
Pronunciation	`assess_pronunciation`	Scores pronunciation at phoneme/word/sentence level (0-100)	257ms p50
Pronunciation	`check_pronunciation_service`	Health check	<100ms
Pronunciation	`get_phoneme_inventory`	Lists all 39 English phonemes with IPA/ARPAbet	<100ms
STT	`transcribe_audio`	Audio to text with word-level timestamps and confidence	~430ms
STT	`check_stt_service`	Health check	<100ms
TTS	`synthesize_speech`	Text to natural speech, 12 voices, speed control	~1.5s
TTS	`list_tts_voices`	Lists available voices with metadata	<100ms
TTS	`check_tts_service`	Health check	<100ms

The pronunciation scorer uses a 17MB proprietary model. It exceeds human expert inter-annotator agreement at phone level (PCC 0.590 vs 0.555) and sentence level (0.711 vs 0.675). The TTS is ranked #1 on TTS Arena with 12 English voices. The STT shares the same 17MB model for word-level timestamps with confidence.

Quick Start: MCP (Claude Desktop, Cursor, Windsurf, etc.)

The fastest path. Your agent gets 8 speech tools through one configuration block.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "speech-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "fabiosuizu/pronunciation-assessment",
        "--config",
        "{\"apimSubscriptionKey\":\"YOUR_KEY\"}"
      ]
    }
  }
}

Cursor / Windsurf

Add to your MCP settings (.cursor/mcp.json or equivalent):

{
  "mcpServers": {
    "speech-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "fabiosuizu/pronunciation-assessment",
        "--config",
        "{\"apimSubscriptionKey\":\"YOUR_KEY\"}"
      ]
    }
  }
}

Direct Streamable HTTP (any MCP client)

If your client supports remote MCP servers:

URL: https://pronunciation-mcp.thankfulfield-a7857897.eastus.azurecontainerapps.io/mcp
Transport: Streamable HTTP

After configuration, your agent has access to all 8 tools. Ask it: "Assess the pronunciation of this audio recording against the text 'Hello world'" and it will call assess_pronunciation automatically.

Quick Start: Python (REST API)

For direct API integration without MCP.

Install

pip install httpx

Pronunciation Assessment

import base64
import httpx

API_BASE = "https://apim-ai-apis.azure-api.net"
HEADERS = {
    "Ocp-Apim-Subscription-Key": "YOUR_KEY",
    "Content-Type": "application/json",
}

# Read and encode audio
with open("recording.wav", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

# Assess pronunciation
response = httpx.post(
    f"{API_BASE}/pronunciation/assess/base64",
    json={"audio": audio_b64, "text": "The quick brown fox", "format": "wav"},
    headers=HEADERS,
    timeout=30.0,
)
result = response.json()

print(f"Overall: {result['overallScore']}/100")
print(f"Confidence: {result['confidence']:.2f}")
for word in result["words"]:
    phonemes = ", ".join(f"{p['phoneme']}={p['score']}" for p in word["phonemes"])
    print(f"  {word['word']}: {word['score']}/100 [{phonemes}]")

Speech-to-Text

response = httpx.post(
    f"{API_BASE}/stt/transcribe/base64",
    json={"audio": audio_b64, "include_timestamps": True},
    headers=HEADERS,
    timeout=30.0,
)
result = response.json()

print(f"Transcript: {result['text']}")
for word in result.get("words", []):
    print(f"  [{word['start']:.2f}s - {word['end']:.2f}s] {word['word']} (conf: {word['confidence']:.2f})")

Text-to-Speech

response = httpx.post(
    f"{API_BASE}/tts/synthesize",
    json={"text": "Hello, how are you today?", "voice": "af_heart", "speed": 1.0, "format": "wav"},
    headers=HEADERS,
    timeout=60.0,
)

# Response is binary WAV
with open("output.wav", "wb") as f:
    f.write(response.content)
print(f"Saved {len(response.content)} bytes to output.wav")

Quick Start: LangChain

Wrap the APIs as LangChain tools for use in any agent framework.

import base64
import httpx
from langchain_core.tools import tool

API_BASE = "https://apim-ai-apis.azure-api.net"
HEADERS = {
    "Ocp-Apim-Subscription-Key": "YOUR_KEY",
    "Content-Type": "application/json",
}

@tool
def assess_pronunciation(audio_path: str, text: str) -> dict:
    """Score English pronunciation at phoneme, word, and sentence level.

    Args:
        audio_path: Path to the audio file (WAV, MP3, OGG, WebM).
        text: The reference English text the speaker was reading.
    """
    with open(audio_path, "rb") as f:
        audio_b64 = base64.b64encode(f.read()).decode()
    resp = httpx.post(
        f"{API_BASE}/pronunciation/assess/base64",
        json={"audio": audio_b64, "text": text, "format": audio_path.split(".")[-1]},
        headers=HEADERS,
        timeout=30.0,
    )
    return resp.json()

@tool
def transcribe_audio(audio_path: str) -> dict:
    """Transcribe audio to text with word-level timestamps.

    Args:
        audio_path: Path to the audio file.
    """
    with open(audio_path, "rb") as f:
        audio_b64 = base64.b64encode(f.read()).decode()
    resp = httpx.post(
        f"{API_BASE}/stt/transcribe/base64",
        json={"audio": audio_b64, "include_timestamps": True},
        headers=HEADERS,
        timeout=30.0,
    )
    return resp.json()

@tool
def synthesize_speech(text: str, voice: str = "af_heart") -> str:
    """Generate natural English speech from text. Returns path to WAV file.

    Args:
        text: English text to speak (max 5000 chars).
        voice: Voice ID. Options: af_heart, af_bella, am_adam, bf_emma, bm_george, etc.
    """
    resp = httpx.post(
        f"{API_BASE}/tts/synthesize",
        json={"text": text, "voice": voice, "speed": 1.0, "format": "wav"},
        headers=HEADERS,
        timeout=60.0,
    )
    path = "/tmp/tts_output.wav"
    with open(path, "wb") as f:
        f.write(resp.content)
    return path

# Use in any LangChain agent
tools = [assess_pronunciation, transcribe_audio, synthesize_speech]

Example: Build a Pronunciation Tutor Agent

A complete pronunciation tutor in ~20 lines. The agent generates a target sentence, speaks it, listens to the student, and gives phoneme-level feedback.

import base64
import httpx

API = "https://apim-ai-apis.azure-api.net"
KEY = {"Ocp-Apim-Subscription-Key": "YOUR_KEY", "Content-Type": "application/json"}

def tutor_session(sentence: str, student_audio_path: str):
    """Run one pronunciation tutoring cycle."""

    # 1. Generate target speech so the student hears correct pronunciation
    tts = httpx.post(f"{API}/tts/synthesize",
        json={"text": sentence, "voice": "af_heart", "speed": 0.9, "format": "wav"},
        headers=KEY, timeout=60)
    with open("target.wav", "wb") as f:
        f.write(tts.content)
    print(f"Listen to target.wav, then record yourself saying: '{sentence}'")

    # 2. Transcribe what the student actually said
    with open(student_audio_path, "rb") as f:
        audio_b64 = base64.b64encode(f.read()).decode()
    stt = httpx.post(f"{API}/stt/transcribe/base64",
        json={"audio": audio_b64}, headers=KEY, timeout=30).json()
    print(f"You said: {stt['text']}")

    # 3. Score pronunciation against the target
    result = httpx.post(f"{API}/pronunciation/assess/base64",
        json={"audio": audio_b64, "text": sentence, "format": "wav"},
        headers=KEY, timeout=30).json()

    # 4. Give feedback
    print(f"\nScore: {result['overallScore']}/100 (confidence: {result['confidence']:.2f})")
    for w in result["words"]:
        weak = [p for p in w["phonemes"] if p["score"] < 60]
        status = "needs work" if weak else "good"
        print(f"  {w['word']}: {w['score']}/100 ({status})")
        for p in weak:
            print(f"    -> /{p['phoneme']}/ scored {p['score']} — practice this sound")

# Run it
tutor_session("The quick brown fox jumps over the lazy dog", "student_recording.wav")

Output:

Listen to target.wav, then record yourself saying: 'The quick brown fox jumps over the lazy dog'
You said: the quick brown fox jumps over the lazy dog

Score: 78/100 (confidence: 0.92)
  the: 85/100 (good)
  quick: 72/100 (needs work)
    -> /K/ scored 45 — practice this sound
  brown: 88/100 (good)
  fox: 90/100 (good)
  jumps: 65/100 (needs work)
    -> /JH/ scored 38 — practice this sound
  over: 82/100 (good)
  the: 80/100 (good)
  lazy: 76/100 (good)
  dog: 91/100 (good)

API Reference

Endpoints

API	Method	URL	Auth
Pronunciation	POST	`https://apim-ai-apis.azure-api.net/pronunciation/assess/base64`	`Ocp-Apim-Subscription-Key`
STT	POST	`https://apim-ai-apis.azure-api.net/stt/transcribe/base64`	`Ocp-Apim-Subscription-Key`
TTS	POST	`https://apim-ai-apis.azure-api.net/tts/synthesize`	`Ocp-Apim-Subscription-Key`

Audio Formats

WAV (recommended), MP3, OGG, WebM, FLAC (STT only). Base64-encoded for JSON endpoints.

TTS Voices

12 voices: af_heart (default), af_bella, af_nicole, af_sarah, af_sky, am_adam, am_michael, bf_emma, bf_isabella, bm_george, bm_lewis, bm_daniel. Speed: 0.5x to 2.0x.

Where to Get It

Channel	URL	Pricing
Smithery	https://smithery.ai/server/fabiosuizu/pronunciation-assessment	Free (MCP discovery)
MCPize	https://mcpize.com/mcp/speech-ai	$9.99/mo
Apify	https://apify.com/vivid_astronaut/pronunciation-assessment-mcp	$0.02/call
Azure Marketplace	Coming soon	Free / Basic / Enterprise tiers
REST API	`apim-ai-apis.azure-api.net`	Contact for key

Demo: https://huggingface.co/spaces/fabiosuizu/pronunciation-assessment

What's Next

Multilingual support (Mandarin tones, Spanish phonetics)
Premium tier for maximum accuracy
Streaming STT for real-time transcription
Browser SDK for direct client-side integration

Contact: fabio@suizu.com | Brainiall

Why Your AI Agent Should Use a Speech API Instead of LLM Inference

Fabio Augusto Suizu — Thu, 26 Feb 2026 00:59:23 +0000

The economics of specialized tools vs. general-purpose reasoning, and what it means for agent architecture.

The Temptation

You're building an AI agent that needs to evaluate a student's English pronunciation. The temptation is obvious: send the audio to your LLM and ask it to score the pronunciation.

This doesn't work. Not because the LLM isn't smart enough, but because it's architecturally incapable of the task. An LLM never sees the audio signal. It sees text tokens. When you ask it to evaluate pronunciation from a transcript, you're asking it to infer acoustic properties from a textual representation that has already discarded all acoustic information.

The result is a confident, plausible, and completely fabricated analysis. The LLM will generate phoneme-level feedback that sounds reasonable but has no basis in the actual audio.

This is not a limitation of current models. It's a category error. Pronunciation scoring requires specialized acoustic models that analyze the audio signal at the phoneme level — computing how closely each sound matches the expected pronunciation. LLMs don't have acoustic models. They don't analyze audio waveforms. They generate text.

The alternative: call a specialized speech API.

The Cost Comparison

Let's make this concrete with three speech tasks an AI agent might need.

Task 1: Pronunciation Assessment

Option A: LLM inference

You feed audio through a pipeline: first transcribe it (using an LLM or separate ASR), then ask an LLM to evaluate pronunciation quality from the transcript.

Even setting aside the fundamental accuracy problem, the economics don't work:

Metric	LLM Approach	Specialized API
Output tokens needed	~2,000 (reasoning + fake scores)	0 (API returns structured JSON)
Cost per call (Opus 4.6)	~$0.15 (2K output tokens at $75/M)	$0.02
Latency	3-8s (generation time)	257ms (p50)
Phoneme-level scores	No (fabricated)	Yes (39 phonemes, IPA + ARPAbet)
Accuracy	Unmeasurable (no acoustic model)	PCC 0.590 (exceeds human experts at 0.555)

The LLM approach costs 7.5x more, takes 10-30x longer, and produces results that have no acoustic grounding.

At scale: 1,000 assessments/day

	LLM (Opus 4.6)	Specialized API
Daily cost	$150	$20
Monthly cost	$4,500	$600
Annual cost	$54,750	$7,300
Annual savings	—	$47,450 (87%)

And the API results are actually accurate.

Task 2: Text-to-Speech

Your agent needs to speak. Options:

Option A: LLM-based TTS

Modern multimodal models can generate audio tokens, but the economics are brutal. Audio generation uses output tokens at the same rate as text, and a 5-second audio clip can consume thousands of tokens.

Option B: Specialized TTS API

Our TTS model generates natural speech at 24kHz. Twelve English voices. Apache 2.0 licensed. Ranked #1 on TTS Arena. Runs on CPU.

Metric	LLM Audio Generation	Speech AI TTS API
Quality ranking	Varies	#1 on TTS Arena
Model size	>100GB (full LLM)	115MB
Latency (short phrase)	5-15s	~1.5s
Voices	1-2	12 (US/UK, M/F)
Speed control	No	0.5x - 2.0x
Cost per synthesis	$0.05-0.50+ (token-dependent)	<$0.01

Task 3: Speech-to-Text

Your agent needs to listen. Options:

Option A: LLM with audio input

Some models accept audio natively. The cost is measured in input tokens, and audio is token-expensive.

Option B: Specialized STT API

A 17MB proprietary model with word-level timestamps and per-word confidence.

Metric	LLM Audio Input	Specialized STT API
Model size	>100GB	17MB
Word timestamps	No	Yes
Per-word confidence	No	Yes
Audio quality metrics	No	Yes
Latency (10s audio)	2-5s	~430ms
Cost (10s audio)	$0.01-0.05	<$0.01

The Aggregate Picture

For an agent that processes 1,000 interactions per day, each involving one pronunciation assessment, one TTS synthesis, and one STT transcription:

	LLM-Only Stack	Specialized APIs
Pronunciation	$150/day	$20/day
TTS	$50-500/day	<$10/day
STT	$10-50/day	<$10/day
Total daily	$210-700	$30-40
Total monthly	$6,300-21,000	$900-1,200
Total annual	$76,650-255,500	$10,950-14,600

The savings range from 85% to 95%. And you get capabilities the LLM approach simply cannot provide: phoneme-level scores, word-level timestamps, voice selection, speed control, audio quality metrics.

Beyond Cost: The Accuracy Gap

Cost is the easy argument. The harder question is accuracy.

For pronunciation scoring, we benchmark on the standard academic dataset — 2,500 utterances scored by 5 human experts each.

Scoring System	Phone PCC	Word PCC	Sentence PCC
Our API	0.590	0.595	0.711
Human expert agreement	0.555	0.618	0.675
Academic SOTA (3MH)	—	0.693	0.811

Our engine exceeds human inter-annotator agreement at phone level (+6.3%) and sentence level (+5.3%). An LLM cannot produce PCC scores at all because it lacks the acoustic model to compute them.

This is not "our API is slightly better." This is "our API produces a category of output that LLMs are structurally incapable of generating."

A phoneme score of 45 on /TH/ means the model measured the acoustic quality of that specific sound against what a correct pronunciation should sound like — and found it deficient. No amount of LLM reasoning can replicate this analysis without a specialized acoustic model.

The Architecture Principle

This analysis points to a broader architectural principle for AI agent systems:

Use LLMs for reasoning. Use specialized tools for perception and generation.

LLMs are extraordinary at:

Understanding context and intent
Planning multi-step actions
Synthesizing information from multiple sources
Generating natural language responses

LLMs are structurally unsuited for:

Computing acoustic features from audio signals
Generating high-fidelity audio waveforms
Producing frame-aligned phoneme scores
Any task requiring real-time signal processing

The optimal agent architecture separates these concerns:

                    +-------------------+
                    |     LLM Brain     |
                    |  (reasoning,      |
                    |   planning,       |
                    |   conversation)   |
                    +---+-----+-----+--+
                        |     |     |
                  tool  |     |     |  tool
                  call  |     |     |  call
                        v     v     v
                +-------+ +---+ +-------+
                | Pron.  | |STT| |  TTS  |
                | API    | |API| |  API  |
                | $0.02  | |   | |       |
                +--------+ +---+ +-------+
                  257ms    430ms   1.5s

The LLM orchestrates. The tools execute. Each component does what it's best at.

MCP: The Delivery Mechanism

The Model Context Protocol is what makes this architecture practical. MCP gives AI agents a standardized way to discover and call external tools.

Instead of writing custom API integration code for each agent framework, you publish your tool once as an MCP server and it works everywhere — Claude Desktop, Cursor, Windsurf, custom agents built with LangChain or CrewAI.

Our Speech AI MCP server exposes 8 tools:

Tool	Purpose
`assess_pronunciation`	Score pronunciation at phoneme/word/sentence level
`transcribe_audio`	Audio to text with word timestamps
`synthesize_speech`	Text to natural speech (12 voices)
`get_phoneme_inventory`	List supported phonemes (IPA + ARPAbet)
`list_tts_voices`	Available voices with metadata
`check_pronunciation_service`	Pronunciation API health
`check_stt_service`	STT API health
`check_tts_service`	TTS API health

Available on:

Smithery: https://smithery.ai/server/fabiosuizu/pronunciation-assessment
MCPize: https://mcpize.com/mcp/speech-ai
Apify: https://apify.com/vivid_astronaut/pronunciation-assessment-mcp

The Trend

We're at the beginning of a structural shift in how AI agents are built.

The first generation of agents tried to do everything through LLM inference. Need to search? Generate a search query and parse results. Need to code? Generate code tokens. Need to assess pronunciation? Generate... a hallucinated analysis.

The second generation recognizes that LLMs are orchestrators, not universal executors. They reason about what needs to happen, then call the right tool to make it happen.

This mirrors how humans work. A doctor doesn't personally run blood tests, X-rays, and MRIs. They reason about symptoms, order the right specialized tests, and synthesize the results into a diagnosis. The doctor is the reasoning engine. The lab equipment are the tools.

AI agents will follow the same pattern. And the specialized tools they call will increasingly be delivered through MCP — a standard protocol that lets any agent discover and use any tool, from any provider.

For speech capabilities specifically, the math is simple: $0.02 and 257ms for phoneme-level scores that exceed human experts. No LLM can match that on cost, speed, or accuracy. Because this isn't what LLMs are designed to do.

Build your agent to reason. Let specialized tools handle the rest.

Try It

Demo: https://huggingface.co/spaces/fabiosuizu/pronunciation-assessment

REST API: https://apim-ai-apis.azure-api.net (Pronunciation, STT, TTS)

MCP Server: Available on Smithery, MCPize, and Apify

Contact: fabio@suizu.com | Brainiall

17MB vs 1.2GB: How a Tiny Model Beats Human Experts at Pronunciation Scoring

Fabio Augusto Suizu — Mon, 23 Feb 2026 19:24:53 +0000

A technical deep-dive into building a pronunciation assessment engine that's 70x smaller than industry standard — and still outperforms human annotators.

The Problem

Pronunciation assessment is a $2.7B market growing at 18% CAGR, driven by 1.5 billion English learners worldwide. Yet the tools available today fall into two buckets:

Cloud-only black boxes (Azure Speech, ELSA Speak) — accurate but expensive, opaque, and locked to specific vendors
Academic models — open but massive (1.2GB+), requiring GPU inference and research-level expertise to deploy

There's nothing in between. No lightweight, self-hostable engine that delivers expert-level accuracy.

We built one.

The Numbers

We benchmarked against the standard academic benchmark for pronunciation assessment — 5,000 utterances scored by 5 expert annotators each.

Metric	Our Engine	Human Experts	Azure Speech	Academic SOTA
Phone-level PCC	0.580	0.555	0.656	0.679
Word-level PCC	0.595	0.618	—	0.693
Sentence-level PCC	0.710	0.675	0.782	0.811
Model size	17 MB	—	Proprietary (est. >1GB)	~360 MB+
Inference p50	257 ms	—	~700 ms	Batch only
Self-hostable	Yes	—	No (cloud containers)	Yes (GPU needed)
Cost per call	$0.003	—	~$0.003/8s	Free (self-host)

Key finding: Our engine exceeds human inter-annotator agreement at phone level (+4.5%) and sentence level (+5.2%), while being 70x smaller than the alternatives used in academia.

How It Works

Our Approach

Instead of using massive foundation models (360MB+) as feature extractors — the dominant approach in academia — we built a proprietary pipeline optimized specifically for pronunciation assessment.

The key insight: Pronunciation scoring doesn't need a general-purpose speech model. It needs accurate phoneme-level analysis and calibration against human expert ratings. By focusing the architecture on exactly what's needed, we achieve expert-level accuracy in a fraction of the size.

The result: A 17MB engine that runs on any CPU in under 300ms, delivering phoneme, word, and sentence-level scores calibrated against thousands of expert-rated utterances.

The industry standard approaches use 360MB+ models to extract general speech features, then train a small scoring head on top. We take a fundamentally different path — one that trades model generality for deployment efficiency without sacrificing the accuracy that matters for pronunciation feedback.

What We Sacrifice

Transparency matters. Here's where the big models win:

Phone PCC: Azure (0.656) and academic SOTA (0.679) beat our 0.580. Larger models capture subtler acoustic distinctions.
Sentence PCC: The best academic model achieves 0.811 vs our 0.710. Multi-level modeling helps at this granularity.
Robustness to noise: Larger models are generally more robust to background noise, though our quality filtering mitigates this.

The trade-off is explicit: we sacrifice ~10-15% relative accuracy for a 70x reduction in model size and the ability to run on any CPU in under 300ms.

Latency Profile

Measured over 2,500 assessments:

Percentile	Our Engine	Azure Speech (warm)
p50	257 ms	~700 ms
p95	423 ms	~2,000 ms
p99	512 ms	~5,000 ms (cold)

Sub-300ms median latency means real-time feedback during conversation — something that's impractical with 700ms+ latency.

Deployment Options

The 17MB footprint enables deployment scenarios impossible with larger models:

Scenario	Our Engine	Standard Academic
Mobile (on-device)	Yes	No
Edge/IoT	Yes	No
Serverless (cold start)	<2s	>10s
Browser (WASM, future)	Feasible	No
Air-gapped environments	Yes	Yes (but 70x storage)

Try It

The API is live with multiple integration options:

REST API: POST /assess with audio file upload or base64 JSON
MCP Server: Direct integration for AI agents (Smithery, Apify, MCPize)
Azure Marketplace: Enterprise billing with SLA (coming soon)
HuggingFace Space: Interactive demo for evaluation

Quick Start

curl -X POST "https://your-endpoint/assess" \
  -F "audio=@recording.wav" \
  -F "text=The quick brown fox jumps over the lazy dog"

Response (simplified)

{
  "overallScore": 82,
  "sentenceScore": 85,
  "confidence": 0.94,
  "words": [
    {
      "word": "quick",
      "score": 90,
      "phonemes": [
        {"phoneme": "K", "score": 95},
        {"phoneme": "W", "score": 88},
        {"phoneme": "IH", "score": 92},
        {"phoneme": "K", "score": 85}
      ]
    }
  ]
}

Conclusion

Pronunciation assessment doesn't need billion-parameter models. A well-designed, purpose-built pipeline delivers expert-level accuracy in 17MB.

The trade-off is clear: we're ~10-15% below SOTA on raw accuracy, but 70x smaller, 2-3x faster, and deployable anywhere. For the vast majority of language learning use cases, this is the right trade-off.

We're currently building a Premium tier for users who need maximum accuracy regardless of model size. But for sub-second feedback at edge scale, the lightweight engine is hard to beat.

Contact: fabio@suizu.com | API access and documentation: https://apim-ai-apis.azure-api.net

Why Your AI Agent Should Use a Speech API Instead of LLM Inference

Fabio Augusto Suizu — Sat, 21 Feb 2026 23:39:37 +0000

The economics of specialized tools vs. general-purpose reasoning, and what it means for agent architecture.

The Temptation

You're building an AI agent that needs to evaluate a student's English pronunciation. The temptation is obvious: send the audio to your LLM and ask it to score the pronunciation.

The result is a confident, plausible, and completely fabricated analysis. The LLM will generate phoneme-level feedback that sounds reasonable but has no basis in the actual audio.

The alternative: call a specialized speech API.

The Cost Comparison

Let's make this concrete with three speech tasks an AI agent might need.

Task 1: Pronunciation Assessment

Option A: LLM inference

You feed audio through a pipeline: first transcribe it (using an LLM or separate ASR), then ask an LLM to evaluate pronunciation quality from the transcript.

Even setting aside the fundamental accuracy problem, the economics don't work:

Metric	LLM Approach	Specialized API
Output tokens needed	~2,000 (reasoning + fake scores)	0 (API returns structured JSON)
Cost per call (Opus 4.6)	~$0.15 (2K output tokens at $75/M)	$0.02
Latency	3-8s (generation time)	257ms (p50)
Phoneme-level scores	No (fabricated)	Yes (39 phonemes, IPA + ARPAbet)
Accuracy	Unmeasurable (no acoustic model)	PCC 0.590 (exceeds human experts at 0.555)

The LLM approach costs 7.5x more, takes 10-30x longer, and produces results that have no acoustic grounding.

At scale: 1,000 assessments/day

	LLM (Opus 4.6)	Specialized API
Daily cost	$150	$20
Monthly cost	$4,500	$600
Annual cost	$54,750	$7,300
Annual savings	—	$47,450 (87%)

And the API results are actually accurate.

Task 2: Text-to-Speech

Your agent needs to speak. Options:

Option A: LLM-based TTS

Option B: Specialized TTS API

Our TTS model generates natural speech at 24kHz. Twelve English voices. Apache 2.0 licensed. Ranked #1 on TTS Arena. Runs on CPU.

Metric	LLM Audio Generation	Speech AI TTS API
Quality ranking	Varies	#1 on TTS Arena
Model size	>100GB (full LLM)	115MB
Latency (short phrase)	5-15s	~1.5s
Voices	1-2	12 (US/UK, M/F)
Speed control	No	0.5x - 2.0x
Cost per synthesis	$0.05-0.50+ (token-dependent)	<$0.01

Task 3: Speech-to-Text

Your agent needs to listen. Options:

Option A: LLM with audio input

Some models accept audio natively. The cost is measured in input tokens, and audio is token-expensive.

Option B: Specialized STT API

A 17MB proprietary model with word-level timestamps and per-word confidence.

Metric	LLM Audio Input	Specialized STT API
Model size	>100GB	17MB
Word timestamps	No	Yes
Per-word confidence	No	Yes
Audio quality metrics	No	Yes
Latency (10s audio)	2-5s	~430ms
Cost (10s audio)	$0.01-0.05	<$0.01

The Aggregate Picture

For an agent that processes 1,000 interactions per day, each involving one pronunciation assessment, one TTS synthesis, and one STT transcription:

	LLM-Only Stack	Specialized APIs
Pronunciation	$150/day	$20/day
TTS	$50-500/day	<$10/day
STT	$10-50/day	<$10/day
Total daily	$210-700	$30-40
Total monthly	$6,300-21,000	$900-1,200
Total annual	$76,650-255,500	$10,950-14,600

The savings range from 85% to 95%. And you get capabilities the LLM approach simply cannot provide: phoneme-level scores, word-level timestamps, voice selection, speed control, audio quality metrics.

Beyond Cost: The Accuracy Gap

Cost is the easy argument. The harder question is accuracy.

For pronunciation scoring, we benchmark on the standard academic dataset — 2,500 utterances scored by 5 human experts each.

Scoring System	Phone PCC	Word PCC	Sentence PCC
Our API	0.590	0.595	0.711
Human expert agreement	0.555	0.618	0.675
Academic SOTA (3MH)	—	0.693	0.811

Our engine exceeds human inter-annotator agreement at phone level (+6.3%) and sentence level (+5.3%). An LLM cannot produce PCC scores at all because it lacks the acoustic model to compute them.

This is not "our API is slightly better." This is "our API produces a category of output that LLMs are structurally incapable of generating."

The Architecture Principle

This analysis points to a broader architectural principle for AI agent systems:

Use LLMs for reasoning. Use specialized tools for perception and generation.

LLMs are extraordinary at:

Understanding context and intent
Planning multi-step actions
Synthesizing information from multiple sources
Generating natural language responses

LLMs are structurally unsuited for:

Computing acoustic features from audio signals
Generating high-fidelity audio waveforms
Producing frame-aligned phoneme scores
Any task requiring real-time signal processing

The optimal agent architecture separates these concerns:

\+-------------------+ | LLM Brain | | (reasoning, | | planning, | | conversation) | +---+-----+-----+--+ | | | tool | | | tool call | | | call v v v +-------+ +---+ +-------+ | Pron. | |STT| | TTS | | API | |API| | API | | $0.02 | | | | | +--------+ +---+ +-------+ 257ms 430ms 1.5s \\

The LLM orchestrates. The tools execute. Each component does what it's best at.

MCP: The Delivery Mechanism

The Model Context Protocol is what makes this architecture practical. MCP gives AI agents a standardized way to discover and call external tools.

Our Speech AI MCP server exposes 8 tools:

Tool	Purpose
`assess_pronunciation\`	Score pronunciation at phoneme/word/sentence level
`transcribe_audio\`	Audio to text with word timestamps
`synthesize_speech\`	Text to natural speech (12 voices)
`get_phoneme_inventory\`	List supported phonemes (IPA + ARPAbet)
`list_tts_voices\`	Available voices with metadata
`check_pronunciation_service\`	Pronunciation API health
`check_stt_service\`	STT API health
`check_tts_service\`	TTS API health

Available on:

Smithery: https://smithery.ai/server/fabiosuizu/pronunciation-assessment
MCPize: https://mcpize.com/mcp/speech-ai
Apify: https://apify.com/vivid_astronaut/pronunciation-assessment-mcp

The Trend

We're at the beginning of a structural shift in how AI agents are built.

The second generation recognizes that LLMs are orchestrators, not universal executors. They reason about what needs to happen, then call the right tool to make it happen.

Build your agent to reason. Let specialized tools handle the rest.

Try It

Demo: https://huggingface.co/spaces/fabiosuizu/pronunciation-assessment

REST API: https://apim-ai-apis.azure-api.net (Pronunciation, STT, TTS)

MCP Server: Available on Smithery, MCPize, and Apify

Contact: fabio@suizu.com | Brainiall

Add Speech to Your AI Agent in 5 Minutes

Fabio Augusto Suizu — Sat, 21 Feb 2026 23:34:10 +0000

Your agent reads text. It writes text. It reasons about text. But it can't hear a student say "hello" and tell them their /h/ is perfect while their /l/ needs work. Here's how to fix that.

The Problem

AI agents are text-native. They consume text, produce text, and reason over text. But a growing class of applications requires agents to interact with the physical world through sound:

Language tutoring agents that listen to a student and give phoneme-level feedback
Customer service agents that transcribe calls and respond with natural speech
Accessibility agents that read content aloud and accept voice commands
Interview practice agents that evaluate spoken responses

What you need are specialized speech tools.

The Solution: Speech AI Tools

We ship three speech APIs as both a REST interface and an MCP server with 8 tools:

Capability	Tool	What It Does	Latency
Pronunciation	`assess_pronunciation`	Scores pronunciation at phoneme/word/sentence level (0-100)	257ms p50
Pronunciation	`check_pronunciation_service`	Health check	<100ms
Pronunciation	`get_phoneme_inventory`	Lists all 39 English phonemes with IPA/ARPAbet	<100ms
STT	`transcribe_audio`	Audio to text with word-level timestamps and confidence	~430ms
STT	`check_stt_service`	Health check	<100ms
TTS	`synthesize_speech`	Text to natural speech, 12 voices, speed control	~1.5s
TTS	`list_tts_voices`	Lists available voices with metadata	<100ms
TTS	`check_tts_service`	Health check	<100ms

Quick Start: MCP (Claude Desktop, Cursor, Windsurf, etc.)

The fastest path. Your agent gets 8 speech tools through one configuration block.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "speech-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "fabiosuizu/pronunciation-assessment",
        "--config",
        "{\\"apimSubscriptionKey\\":\\"YOUR_KEY\\"}"
      ]
    }
  }
}

Cursor / Windsurf

Add to your MCP settings (.cursor/mcp.json or equivalent):

{
  "mcpServers": {
    "speech-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "fabiosuizu/pronunciation-assessment",
        "--config",
        "{\\"apimSubscriptionKey\\":\\"YOUR_KEY\\"}"
      ]
    }
  }
}

Direct Streamable HTTP (any MCP client)

If your client supports remote MCP servers:

URL: https://pronunciation-mcp.thankfulfield-a7857897.eastus.azurecontainerapps.io/mcp
Transport: Streamable HTTP

Quick Start: Python (REST API)

For direct API integration without MCP.

Install

pip install httpx

Pronunciation Assessment

import base64
import httpx

API_BASE = "https://apim-ai-apis.azure-api.net"
HEADERS = {
    "Ocp-Apim-Subscription-Key": "YOUR_KEY",
    "Content-Type": "application/json",
}

# Read and encode audio
with open("recording.wav", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

# Assess pronunciation
response = httpx.post(
    f"{API_BASE}/pronunciation/assess/base64",
    json={"audio": audio_b64, "text": "The quick brown fox", "format": "wav"},
    headers=HEADERS,
    timeout=30.0,
)
result = response.json()

print(f"Overall: {result['overallScore']}/100")
print(f"Confidence: {result['confidence']:.2f}")
for word in result["words"]:
    phonemes = ", ".join(f"{p['phoneme']}={p['score']}" for p in word["phonemes"])
    print(f"  {word['word']}: {word['score']}/100 [{phonemes}]")

Speech-to-Text

response = httpx.post(
    f"{API_BASE}/stt/transcribe/base64",
    json={"audio": audio_b64, "include_timestamps": True},
    headers=HEADERS,
    timeout=30.0,
)
result = response.json()

print(f"Transcript: {result['text']}")
for word in result.get("words", []):
    print(f"  [{word['start']:.2f}s - {word['end']:.2f}s] {word['word']} (conf: {word['confidence']:.2f})")

Text-to-Speech

response = httpx.post(
    f"{API_BASE}/tts/synthesize",
    json={"text": "Hello, how are you today?", "voice": "af_heart", "speed": 1.0, "format": "wav"},
    headers=HEADERS,
    timeout=60.0,
)

# Response is binary WAV
with open("output.wav", "wb") as f:
    f.write(response.content)
print(f"Saved {len(response.content)} bytes to output.wav")

Quick Start: LangChain

Wrap the APIs as LangChain tools for use in any agent framework.

import base64
import httpx
from langchain_core.tools import tool

API_BASE = "https://apim-ai-apis.azure-api.net"
HEADERS = {
    "Ocp-Apim-Subscription-Key": "YOUR_KEY",
    "Content-Type": "application/json",
}

@tool
def assess_pronunciation(audio_path: str, text: str) -> dict:
    """Score English pronunciation at phoneme, word, and sentence level."""
    with open(audio_path, "rb") as f:
        audio_b64 = base64.b64encode(f.read()).decode()
    resp = httpx.post(
        f"{API_BASE}/pronunciation/assess/base64",
        json={"audio": audio_b64, "text": text, "format": audio_path.split(".")[-1]},
        headers=HEADERS,
        timeout=30.0,
    )
    return resp.json()

@tool
def transcribe_audio(audio_path: str) -> dict:
    """Transcribe audio to text with word-level timestamps."""
    with open(audio_path, "rb") as f:
        audio_b64 = base64.b64encode(f.read()).decode()
    resp = httpx.post(
        f"{API_BASE}/stt/transcribe/base64",
        json={"audio": audio_b64, "include_timestamps": True},
        headers=HEADERS,
        timeout=30.0,
    )
    return resp.json()

@tool
def synthesize_speech(text: str, voice: str = "af_heart") -> str:
    """Generate natural English speech from text. Returns path to WAV file."""
    resp = httpx.post(
        f"{API_BASE}/tts/synthesize",
        json={"text": text, "voice": voice, "speed": 1.0, "format": "wav"},
        headers=HEADERS,
        timeout=60.0,
    )
    path = "/tmp/tts_output.wav"
    with open(path, "wb") as f:
        f.write(resp.content)
    return path

# Use in any LangChain agent
tools = [assess_pronunciation, transcribe_audio, synthesize_speech]

Example: Build a Pronunciation Tutor Agent

A complete pronunciation tutor in ~20 lines:

import base64
import httpx

API = "https://apim-ai-apis.azure-api.net"
KEY = {"Ocp-Apim-Subscription-Key": "YOUR_KEY", "Content-Type": "application/json"}

def tutor_session(sentence: str, student_audio_path: str):
    # 1. Generate target speech
    tts = httpx.post(f"{API}/tts/synthesize",
        json={"text": sentence, "voice": "af_heart", "speed": 0.9, "format": "wav"},
        headers=KEY, timeout=60)
    with open("target.wav", "wb") as f:
        f.write(tts.content)
    print(f"Listen to target.wav, then record yourself saying: '{sentence}'")

    # 2. Transcribe what the student said
    with open(student_audio_path, "rb") as f:
        audio_b64 = base64.b64encode(f.read()).decode()
    stt = httpx.post(f"{API}/stt/transcribe/base64",
        json={"audio": audio_b64}, headers=KEY, timeout=30).json()
    print(f"You said: {stt['text']}")

    # 3. Score pronunciation
    result = httpx.post(f"{API}/pronunciation/assess/base64",
        json={"audio": audio_b64, "text": sentence, "format": "wav"},
        headers=KEY, timeout=30).json()

    # 4. Give feedback
    print(f"Score: {result['overallScore']}/100")
    for w in result["words"]:
        weak = [p for p in w["phonemes"] if p["score"] < 60]
        status = "needs work" if weak else "good"
        print(f"  {w['word']}: {w['score']}/100 ({status})")
        for p in weak:
            print(f"    -> /{p['phoneme']}/ scored {p['score']}")

tutor_session("The quick brown fox", "student_recording.wav")

Where to Get It

Channel	URL	Pricing
Smithery	https://smithery.ai/server/fabiosuizu/pronunciation-assessment	Free (MCP)
MCPize	https://mcpize.com/mcp/speech-ai	$9.99/mo
Apify	https://apify.com/vivid_astronaut/pronunciation-assessment-mcp	$0.02/call
Azure Marketplace	Coming soon	Free / Basic / Enterprise
REST API	apim-ai-apis.azure-api.net	Contact for key

Demo: https://huggingface.co/spaces/fabiosuizu/pronunciation-assessment

Contact: fabio@suizu.com | Brainiall

17MB vs 1.2GB: How a Tiny Model Beats Human Experts at Pronunciation Scoring

Fabio Augusto Suizu — Sat, 21 Feb 2026 23:29:06 +0000

A technical deep-dive into building a pronunciation assessment engine that's 70x smaller than industry standard — and still outperforms human annotators.

The Problem

Pronunciation assessment is a $2.7B market growing at 18% CAGR, driven by 1.5 billion English learners worldwide. Yet the tools available today fall into two buckets:

Cloud-only black boxes (Azure Speech, ELSA Speak) — accurate but expensive, opaque, and locked to specific vendors
Academic models — open but massive (1.2GB+), requiring GPU inference and research-level expertise to deploy

There's nothing in between. No lightweight, self-hostable engine that delivers expert-level accuracy.

We built one.

The Numbers

We benchmarked against the standard academic benchmark for pronunciation assessment — 5,000 utterances scored by 5 expert annotators each.

Metric	Our Engine	Human Experts	Azure Speech	Academic SOTA
Phone-level PCC	0.580	0.555	0.656	0.679
Word-level PCC	0.595	0.618	—	0.693
Sentence-level PCC	0.710	0.675	0.782	0.811
Model size	17 MB	—	Proprietary (est. >1GB)	~360 MB+
Inference p50	257 ms	—	~700 ms	Batch only
Self-hostable	Yes	—	No (cloud containers)	Yes (GPU needed)
Cost per call	$0.003	—	~$0.003/8s	Free (self-host)

Key finding: Our engine exceeds human inter-annotator agreement at phone level (+4.5%) and sentence level (+5.2%), while being 70x smaller than the alternatives used in academia.

How It Works

Our Approach

The result: A 17MB engine that runs on any CPU in under 300ms, delivering phoneme, word, and sentence-level scores calibrated against thousands of expert-rated utterances.

What We Sacrifice

Transparency matters. Here's where the big models win:

Phone PCC: Azure (0.656) and academic SOTA (0.679) beat our 0.580. Larger models capture subtler acoustic distinctions.
Sentence PCC: The best academic model achieves 0.811 vs our 0.710. Multi-level modeling helps at this granularity.
Robustness to noise: Larger models are generally more robust to background noise, though our quality filtering mitigates this.

The trade-off is explicit: we sacrifice ~10-15% relative accuracy for a 70x reduction in model size and the ability to run on any CPU in under 300ms.

Latency Profile

Measured over 2,500 assessments:

Percentile	Our Engine	Azure Speech (warm)
p50	257 ms	~700 ms
p95	423 ms	~2,000 ms
p99	512 ms	~5,000 ms (cold)

Sub-300ms median latency means real-time feedback during conversation — something that's impractical with 700ms+ latency.

Deployment Options

The 17MB footprint enables deployment scenarios impossible with larger models:

Scenario	Our Engine	Standard Academic
Mobile (on-device)	Yes	No
Edge/IoT	Yes	No
Serverless (cold start)	<2s	>10s
Browser (WASM, future)	Feasible	No
Air-gapped environments	Yes	Yes (but 70x storage)

Try It

The API is live with multiple integration options:

REST API: POST /assess with audio file upload or base64 JSON
MCP Server: Direct integration for AI agents (Smithery, Apify, MCPize)
Azure Marketplace: Enterprise billing with SLA (coming soon)
HuggingFace Space: Interactive demo for evaluation

Quick Start

curl -X POST "https://apim-ai-apis.azure-api.net/pronunciation/assess" \
  -F "audio=@recording.wav" \
  -F "text=The quick brown fox jumps over the lazy dog"

Response (simplified)

{
  "overallScore": 82,
  "sentenceScore": 85,
  "confidence": 0.94,
  "words": [
    {
      "word": "quick",
      "score": 90,
      "phonemes": [
        {"phoneme": "K", "score": 95},
        {"phoneme": "W", "score": 88},
        {"phoneme": "IH", "score": 92},
        {"phoneme": "K", "score": 85}
      ]
    }
  ]
}

Conclusion

Pronunciation assessment doesn't need billion-parameter models. A well-designed, purpose-built pipeline delivers expert-level accuracy in 17MB.

We're currently building a Premium tier for users who need maximum accuracy regardless of model size. But for sub-second feedback at edge scale, the lightweight engine is hard to beat.

Contact: fabio@suizu.com | API access and documentation: https://apim-ai-apis.azure-api.net

17MB vs 1.2GB: How a Tiny Model Beats Human Experts at Pronunciation Scoring

Fabio Augusto Suizu — Fri, 20 Feb 2026 06:21:38 +0000

A technical deep-dive into building a pronunciation assessment engine that's 70x smaller than industry standard — and still outperforms human annotators.

The Problem

Pronunciation assessment is a $2.7B market growing at 18% CAGR, driven by 1.5 billion English learners worldwide. Yet the tools available today fall into two buckets:

Cloud-only black boxes (Azure Speech, ELSA Speak) — accurate but expensive, opaque, and locked to specific vendors
Academic models (wav2vec2 + GOPT) — open but massive (1.2GB+), requiring GPU inference and research-level expertise to deploy

There's nothing in between. No lightweight, self-hostable engine that delivers expert-level accuracy.

We built one.

The Numbers

We benchmarked against the speechocean762 dataset — the standard benchmark for pronunciation assessment, with 5,000 utterances scored by 5 expert annotators each.

Metric	Our Engine	Human Experts	GOPT (Academic)	3MH (SOTA)
Phone-level PCC	0.580	0.555	0.679	—
Word-level PCC	0.595	0.618	0.606	0.693
Sentence-level PCC	0.710	0.675	0.743	0.811
Model size	17 MB	—	~360 MB	~360 MB
Inference p50	257 ms	—	Batch only	Batch only

Key finding: Our engine exceeds human inter-annotator agreement at phone level (+4.5%) and sentence level (+5.2%), while being 70x smaller than wav2vec2-based alternatives.

How It Works

Architecture: CTC + Forced Alignment + GOP

Instead of using a massive self-supervised model (wav2vec2, HuBERT) as a feature extractor, we use a fundamentally different approach:

1. Acoustic Model: NVIDIA NeMo Citrinet-256, quantized to INT4. This 17MB CTC model converts audio to character-level probabilities.

2. Forced Alignment: Viterbi algorithm aligns the audio frames to the expected phoneme sequence using a CMU pronunciation dictionary.

3. GOP Scoring: Goodness-of-Pronunciation scores are computed from the CTC posterior probabilities — comparing "how likely was this phoneme?" against "how likely was any other phoneme at this position?"

4. Ensemble Scoring: MLP and XGBoost heads combine GOP features with acoustic features to produce phone/word/sentence scores calibrated against human expert ratings.

Why This Works

The key insight is that pronunciation scoring doesn't need a general-purpose speech model. It needs:

Accurate phoneme posteriors (CTC provides these at 17MB)
Linguistic knowledge of expected pronunciation (CMU dictionary provides this)
Calibration against human ratings (our ensemble provides this)

The wav2vec2-based approaches use a 360MB+ model to extract general speech features, then train a small head on top. We skip the 360MB and go directly to what matters: phoneme-level posterior probabilities.

What We Sacrifice

Transparency matters. Here's where the big models win:

Phone PCC: GOPT (0.679) beats our 0.580. Their wav2vec2 features capture subtler acoustic distinctions.
Sentence PCC: 3MH's hierarchical transformer achieves 0.811 vs our 0.710. Multi-level modeling helps at this granularity.
Robustness to noise: Larger models are generally more robust to background noise, though our SNR quality gate mitigates this.

The trade-off is explicit: we sacrifice ~10-15% relative accuracy for a 70x reduction in model size and the ability to run on any CPU in under 300ms.

Latency Profile

Measured over 2,500 assessments:

Percentile	Our Engine	Azure Speech (warm)
p50	257 ms	~700 ms
p95	423 ms	~2,000 ms
p99	512 ms	~5,000 ms (cold)

Sub-300ms median latency means real-time feedback during conversation — something that's impractical with 700ms+ latency.

Deployment Options

The 17MB footprint enables deployment scenarios impossible with larger models:

Scenario	Our Engine	wav2vec2-based
Mobile (on-device)	✅	❌
Edge/IoT	✅	❌
Serverless (cold start)	<2s	>10s
Browser (WASM, future)	Feasible	❌
Air-gapped environments	✅	✅ (but 70x storage)

Try It

The API is live with multiple integration options:

REST API: Direct integration with audio file upload or base64 JSON
MCP Server: Direct integration for AI agents (Smithery, Apify, MCPize)
Azure Marketplace: Enterprise billing with SLA (coming soon)
HuggingFace Space: Interactive demo for evaluation

Quick Start

curl -X POST "https://your-endpoint/assess" \
  -F "audio=@recording.wav" \
  -F "text=The quick brown fox jumps over the lazy dog"

Response (simplified)

{
  "overallScore": 82,
  "sentenceScore": 85,
  "confidence": 0.94,
  "words": [
    {
      "word": "quick",
      "score": 90,
      "phonemes": [
        {"phoneme": "K", "score": 95},
        {"phoneme": "W", "score": 88},
        {"phoneme": "IH", "score": 92},
        {"phoneme": "K", "score": 85}
      ]
    }
  ]
}

Conclusion

Pronunciation assessment doesn't need billion-parameter models. A well-designed pipeline — CTC posteriors, forced alignment, GOP scoring, and calibrated ensemble heads — delivers expert-level accuracy in 17MB.

We're currently building a Premium tier using wav2vec2 fine-tuning for users who need maximum accuracy. But for sub-second feedback at edge scale, the lightweight engine is hard to beat.

Built by Brainiall | Try the demo | API access: brainiall.com