Feedback: guides-talk-listen-ratio

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/talk-listen-ratio
Category: guides
Generated: 05/08/2025, 4:36:23 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:36:22 pm

Technical Documentation Analysis & Feedback

Overall Assessment

This documentation provides a functional code example but lacks depth in explanation, context, and user guidance. Here’s my detailed feedback:

🔴 Critical Issues

1. Missing Conceptual Foundation

Problem: The documentation jumps straight into code without explaining what a talk/listen ratio is or why it’s useful.

Fix: Add a concept overview section:

## What is Talk/Listen Ratio?

The talk/listen ratio measures how much one speaker talks compared to others in a conversation. A ratio of:
- **1.0** = Speaker talks equal time to all others combined
- **> 1.0** = Speaker dominates the conversation
- **< 1.0** = Speaker listens more than they talk

**Use Cases:**
- Sales call analysis (identify who drove the conversation)
- Meeting effectiveness (ensure balanced participation)
- Customer service quality assessment
- Interview analysis

2. Incomplete Error Handling

Problem: Only checks for missing utterances, but doesn’t handle API failures or invalid audio.

Fix: Add comprehensive error handling:

def calculate_talk_listen_ratios(transcript):
    # Check if transcription was successful
    if transcript.error:
        raise ValueError(f"Transcription failed: {transcript.error}")

    # Check if speaker labels were enabled
    if not hasattr(transcript, 'utterances') or not transcript.utterances:
        raise ValueError("Speaker labels were not enabled or no speakers detected.")

    # Existing code...

🟡 Structure & Organization Issues

3. Redundant Code Blocks

Problem: The quickstart section duplicates the step-by-step code, creating maintenance burden.

Fix: Restructure as:

## Overview
[Brief explanation of what we'll build]

## Prerequisites
- AssemblyAI account and API key
- Python 3.7+ installed
- Audio file with multiple speakers

## Complete Example
[Full working code]

## Code Breakdown
[Explain each section with focused snippets]

4. Poor Information Hierarchy

Problem: “Get started” section appears after the main code, disrupting logical flow.

Fix: Reorder sections:

1. Overview & Prerequisites
2. Installation & Setup
3. Complete Example
4. Code Explanation
5. Advanced Usage

🟡 Missing Information

5. No Input Requirements

Problem: Users don’t know what audio formats, lengths, or characteristics work best.

Fix: Add requirements section:

## Audio Requirements

**Supported formats:** WAV, MP3, MP4, M4A, FLAC
**Optimal conditions:**
- Clear audio with minimal background noise
- At least 2 distinct speakers
- Minimum 30 seconds duration for meaningful ratios
- Speaker changes should be longer than 1-2 seconds

6. Missing Output Explanation

Problem: Users get a dictionary but don’t understand what the values mean.

Fix: Add detailed output documentation:

## Understanding the Results

```python
{
    'Speaker A': {
        'talk_time_ms': 244196,      # Total milliseconds this speaker talked
        'percentage': 42.77,         # Percentage of total conversation time
        'talk_listen_ratio': 0.75    # Ratio vs all other speakers combined
    }
}

Interpreting talk_listen_ratio:

• 0.75 = Speaker A talked 75% as much as all other speakers combined
• This suggests Speaker A was more of a listener in this conversation

## 🟡 User Experience Issues

### 7. No Practical Examples
**Problem:** Only shows raw output without business context.

**Fix:** Add interpretation examples:
```markdown
## Real-World Examples

### Sales Call Analysis
```python
# If talk_listen_ratio > 1.2 for salesperson
print("⚠️  Salesperson may be over-talking. Consider more discovery questions.")

# If customer ratio < 0.3
print("💡 Customer engagement is low. Try different conversation approach.")

Meeting Balance Check

def analyze_meeting_balance(stats):
    ratios = [speaker['talk_listen_ratio'] for speaker in stats.values()]
    if max(ratios) > 2.0:
        print("Meeting dominated by one speaker")
    elif all(0.5 <= ratio <= 1.5 for ratio in ratios):
        print("Well-balanced discussion")

8. Missing Troubleshooting

Problem: No guidance for common issues users will encounter.

Fix: Add troubleshooting section:

## Troubleshooting

**"Speaker labels were not enabled"**
- Ensure `speaker_labels=True` in TranscriptionConfig
- Check that your audio has multiple distinct speakers

**All speakers show as "Speaker A"**
- Audio may not have sufficient speaker separation
- Try audio with clearer speaker distinctions
- Minimum 1-2 seconds between speaker changes

**Ratios seem incorrect**
- Check for overlapping speech or background noise
- Verify audio quality meets requirements

🟢 Suggested Improvements

9. Add Advanced Features

def enhanced_talk_listen_analysis(transcript, min_utterance_duration=1000):
    """Enhanced version with filtering and additional metrics"""

    # Filter out very short utterances (likely noise/artifacts)
    filtered_utterances = [
        u for u in transcript.utterances
        if (u.end - u.start) >= min_utterance_duration
    ]

    # Add turn-taking analysis
    speaker_turns = {}
    previous_speaker = None

    for utterance in filtered_utterances:
        current_speaker = f"Speaker {utterance.speaker}"
        if current_speaker != previous_speaker:
            speaker_turns[current_speaker] = speaker_turns.get(current_speaker, 0) + 1
            previous_speaker = current_speaker

    # Include turn data in results
    # ... existing calculation code ...

    for speaker in result.keys():
        result[speaker]["turn_count"] = speaker_turns.get(speaker, 0)
        result[speaker]["avg_utterance_length"] = (
            result[speaker]["talk_time_ms"] / speaker_turns.get(speaker, 1)
        )

    return result

10. Add Visualization Suggestion

## Visualizing Results (Optional)

For better insights, consider visualizing the data:

```python
import matplotlib.pyplot as plt

def plot_talk_ratios(stats):
    speakers = list(stats.keys())
    ratios = [stats[speaker]['talk_listen_ratio'] for speaker in speakers]

    plt.bar(speakers, ratios)
    plt.axhline(y=1.0, color='r', linestyle='--', label='Balanced (1.0)')
    plt.ylabel('Talk/Listen Ratio')
    plt.title('Speaker Talk/Listen Ratios')
    plt.legend()
    plt.show()

Summary of Priority Fixes

1. Add conceptual overview (Critical - helps users understand purpose)
2. Improve error handling (Critical - prevents user frustration)
3. Restructure content flow (High - improves usability)
4. Add output explanation (High - essential for interpretation)
5. Include troubleshooting (Medium - reduces support burden)
6. Add practical examples (Medium - shows real-world value)

These improvements would transform this from a basic code example into comprehensive, user-friendly documentation that guides users from concept to implementation to practical application.

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