Feedback: speech-to-text-universal-streaming-message-sequence

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/speech-to-text/universal-streaming/message-sequence
Category: speech-to-text
Generated: 05/08/2025, 4:22:55 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:22:54 pm

Technical Documentation Analysis & Feedback

Overall Assessment

This documentation provides a good visual demonstration of the streaming API message sequence, but lacks critical context and guidance that users need to effectively implement and understand the system.

Critical Missing Information

1. Conceptual Overview

Issue: Users are thrown directly into JSON examples without understanding the fundamental concepts.

Solution: Add an introductory section:

## Overview
The Universal Streaming API processes speech in real-time, sending multiple messages as it refines its understanding of what was spoken. This page shows how a single utterance ("Hi, my name is Sonny") evolves from initial partial transcripts to the final formatted result.

### Key Concepts
- **Partial Transcripts**: Intermediate results as speech is being processed
- **Final Transcripts**: Completed transcription with two versions (unformatted and formatted)
- **Turn**: A complete speech segment from one speaker
- **Word Finalization**: Individual words become "final" before the entire turn is complete

2. Timeline Units

Issue: Timestamp values (1440, 1520, etc.) have no explanation of units.

Solution: Add a note before the first example:

<Note>
  Timestamps in the `start` and `end` fields are in milliseconds from the beginning of the audio stream.
</Note>

3. Message Flow Logic

Issue: No explanation of when/why messages are sent.

Solution: Add explanatory text between examples:

### Message 1: First Word Detected
The API detects the first word "hi" but keeps `word_is_final: false` as it's still processing.

### Message 2: Word Becomes Final
Notice that "hi" now has `word_is_final: true`, indicating the API is confident in this transcription. A new word "name" appears but isn't final yet.

Structural Improvements

1. Add Progressive Annotations

Instead of raw JSON blocks, annotate key changes:

{
  "turn_order": 0,
  "turn_is_formatted": false,
  "end_of_turn": false,  // ← Still processing this turn
  "transcript": "hi my name is",
  "end_of_turn_confidence": 0.017141787335276604,  // ← Low confidence = more speech expected
  "words": [
    // ... previous words now final
    {
      "start": 2320,
      "end": 2400,
      "text": "son",  // ← Partial word, will likely change
      "confidence": 0.471368670463562,  // ← Low confidence
      "word_is_final": false  // ← Still being refined
    }
  ]
}

2. Visual Timeline

Add a visual representation:

## Timeline Visualization

0ms 1440ms 1600ms 1680ms 2320ms 3040ms | | | | | | | “hi” “my” “name” “is” “sonny” | ↓ ↓ ↓ ↓ ↓ | final final final final final

### 3. **Summary Table**
Add a comparison table:

| Message # | Transcript State | Key Changes | End of Turn Confidence |
|-----------|------------------|-------------|------------------------|
| 1 | "hi" | First word detected | 0.68 |
| 2 | "hi my" | "hi" becomes final, "my" added | 0.004 |
| ... | ... | ... | ... |

## User Experience Pain Points

### 1. **No Error Handling Examples**
**Issue**: Only shows successful transcription.

**Solution**: Add a section:
```markdown
## Handling Corrections and Revisions
Sometimes the API will revise earlier words. Here's how a correction looks:

[Show example where earlier "final" words change in subsequent messages]

2. No Implementation Guidance

Issue: Developers don’t know how to handle these messages in code.

Solution: Add practical examples:

## Implementation Tips

### Handling Partial Transcripts
```javascript
function handlePartialTranscript(message) {
  // Only display words that are final for stable UI
  const finalWords = message.words.filter(word => word.word_is_final);
  displayText(finalWords.map(w => w.text).join(' '));
}

3. Missing Performance Context

Issue: No information about message frequency or volume.

Solution: Add:

## Message Frequency
- Partial transcripts: Sent every 100-200ms during active speech
- Final transcripts: Sent when turn completion is detected
- Formatted transcripts: Sent immediately after unformatted final transcript

Content Clarity Issues

1. Inconsistent Data

Issue: Some start/end times are identical between different words (1600ms for both “my” and “name”).

Solution: Either fix the data or explain why this occurs:

<Note>
  Words may share identical timestamps when they're detected simultaneously or when timestamp precision is insufficient for very fast speech.
</Note>

2. Confidence Score Context

Issue: No guidance on interpreting confidence values.

Solution: Add:

## Understanding Confidence Scores
- **0.9+**: High confidence, rarely changes
- **0.7-0.9**: Good confidence, may occasionally be revised
- **Below 0.7**: Lower confidence, more likely to change in subsequent messages

Quick Wins

1. Add a “What’s Next” section linking to implementation guides
2. Include common gotchas (e.g., “Don’t assume word_is_final means it won’t change”)
3. Add filtering examples showing how to extract only completed text
4. Include troubleshooting for common integration issues

Recommended Structure

# Streaming API: Message Sequence Breakdown

## Overview
[Conceptual explanation]

## Understanding the Timeline
[Visual timeline and timestamp explanation]

## Partial Transcripts: Step by Step
[Current examples with annotations]

## Final Transcripts
[Current examples with explanations]

## Implementation Guide
[Code examples and best practices]

## Common Scenarios
[Edge cases, corrections, error handling]

## Troubleshooting
[Common issues and solutions]

This restructured approach would transform the documentation from a simple example dump into a comprehensive guide that helps users both understand the concept and implement it successfully.

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