Feedback: speech-to-text-pre-recorded-audio-automatic-language-detection

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/speech-to-text/pre-recorded-audio/automatic-language-detection
Category: speech-to-text
Generated: 05/08/2025, 4:25:59 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:25:58 pm

Technical Documentation Analysis: Automatic Language Detection

Here’s my comprehensive feedback to improve this documentation:

1. Missing Critical Information

API Response Structure

• Issue: No complete example of the API response showing all language detection fields
• Solution: Add a clear response example:

{
  "id": "abc123",
  "status": "completed",
  "text": "Hello, this is a test...",
  "language_code": "en",
  "language_confidence": 0.95,
  "language_confidence_threshold": 0.8
}

Supported Languages List

• Issue: References “supported languages” but doesn’t list them or provide quick access
• Solution: Add a summary table of commonly supported languages and/or embed the most popular ones directly

Error Handling Details

• Issue: Limited information about what happens when confidence threshold isn’t met
• Solution: Add specific error response examples and error codes

2. Structure and Organization Issues

Overwhelming Code Blocks

• Issue: Massive code blocks for 7+ languages make scanning difficult
• Solution:
  • Lead with 2-3 most popular languages (Python SDK, JavaScript SDK, cURL)
  • Move others to a collapsible “More Languages” section
  • Add a quick reference table showing just the key parameters

Missing Quick Start Section

• Solution: Add a “Quick Start” section before code examples:

## Quick Start
1. Set `language_detection: true` in your request
2. Ensure audio has 15+ seconds of speech
3. Access detected language via `language_code` field
4. Check confidence with `language_confidence` field

3. Clarity and Explanation Improvements

Better Feature Overview

Current: “Identify the dominant language spoken in an audio file and use it during the transcription.”

Improved:

Automatic language detection analyzes your audio file to:
- Identify the primary spoken language
- Automatically select the best transcription model for that language
- Return the detected language code (e.g., "en", "es", "fr")
- Provide a confidence score (0.0-1.0) for the detection accuracy

Confidence Score Explanation Needs Enhancement

• Add: What constitutes “good” vs “poor” confidence scores
• Add: Recommended threshold ranges for different use cases
• Add: What factors affect confidence (audio quality, accent, etc.)

4. User Experience Pain Points

Missing Troubleshooting Section

Add common issues and solutions:

## Troubleshooting
- **Low confidence scores**: Ensure clear audio with minimal background noise
- **Wrong language detected**: Verify the language is in our supported list
- **Detection failed**: Check that audio contains 15+ seconds of actual speech

No Performance/Limitations Info

• Add: Processing time impact when using language detection
• Add: Which languages have the highest accuracy
• Add: File size or duration limits

Missing Use Cases and Best Practices

## When to Use Language Detection
- ✅ Multi-language content platforms
- ✅ International customer support
- ✅ Unknown source audio files
- ❌ When you already know the language (adds processing time)
- ❌ Very short audio clips (<15 seconds)

5. Improved Examples Needed

Real-World Scenarios

Replace generic examples with:

• Customer service call analysis
• Podcast transcription workflow
• Educational content processing

Error Handling Examples

Show complete error handling for threshold failures:

try:
    transcript = aai.Transcriber(config=config).transcribe(audio_file)
    if transcript.status == "error":
        if "language confidence" in transcript.error.lower():
            # Handle low confidence - maybe retry with default language
            fallback_config = aai.TranscriptionConfig(language_code="en")
            transcript = aai.Transcriber(config=fallback_config).transcribe(audio_file)
except Exception as e:
    print(f"Transcription failed: {e}")

6. Technical Improvements

Missing cURL Examples

Many developers prefer cURL for testing - add basic cURL examples.

Incomplete C# Example

The C# code in the first section is missing the language_code output and proper structure.

SDK vs REST API Clarity

Better distinguish between SDK convenience methods and direct API calls.

7. Specific Actionable Changes

1. Add a comparison table showing parameter names across SDKs
2. Include response time estimates (e.g., “adds ~5-10 seconds to processing”)
3. Link to language codes reference (ISO codes explanation)
4. Add webhook example for async processing
5. Include cost implications if language detection affects pricing
6. Add integration examples with popular frameworks (Flask, Express, etc.)

8. Content Flow Recommendation

1. Brief overview with key benefits
2. Quick start checklist
3. Basic example (Python SDK only)
4. Key concepts (confidence, thresholds)
5. Complete examples (top 3 languages)
6. Advanced configuration
7. Troubleshooting
8. More language examples (collapsible)

This restructure would significantly improve user experience while maintaining comprehensive coverage.

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