Can AI Transcribe Dialects? Complete Guide to Dialect Recognition in Speech-to-Text

What Are Dialects and Why Are They Challenging?

Understanding Dialects vs. Accents

• Vocabulary (words and expressions)
• Grammar (sentence structure)
• Pronunciation (how words are spoken)
• Phonology (sound patterns)

• Dialect: Scottish English ("I'm going to the shops" vs. "I'm gaun tae the shops")
• Accent: British vs. American English (same words, different pronunciation)

Why Dialects Challenge AI Transcription

1. Limited Training Data
   • Most AI models are trained on standard language varieties
   • Dialectal speech is underrepresented in training datasets
   • Regional variations may be completely absent
2. Phonetic Variations
   • Different sound patterns than standard speech
   • Unfamiliar phoneme sequences
   • Merged or split sounds
3. Vocabulary Differences
   • Regional words not in standard dictionaries
   • Slang and colloquialisms
   • Code-switching between languages
4. Grammar Variations
   • Non-standard sentence structures
   • Different word orders
   • Unique grammatical constructions

How Modern AI Models Handle Dialects

OpenAI Whisper

• Trained on diverse, real-world audio (680,000 hours)
• Includes various accents and regional speech
• Handles many English dialects reasonably well
• Better with major dialects (British, Australian, Indian English)
• Can transcribe non-standard pronunciations

• Struggles with very regional or rare dialects
• May standardize dialectal words to standard forms
• Less accurate with heavy dialectal features
• Performance varies significantly by dialect

import whisper

model = whisper.load_model("base")

# Scottish dialect example
result = model.transcribe("scottish_accent.wav")
# May transcribe "gaun" as "going" or "gan"
# May miss dialectal vocabulary

• Use larger models (medium, large) for better dialect handling
• Provide context if possible
• Accept that some dialectal features may be standardized

Google Speech-to-Text

• Extensive dialect support for major languages
• Regional model variants (e.g., US, UK, Australian English)
• Good handling of common accents
• Continuous updates with new dialect data

• Requires manual language/dialect selection
• Limited support for rare dialects
• May not preserve dialectal vocabulary

• English: en-US, en-GB, en-AU, en-IN, en-NZ, en-ZA
• Spanish: es-ES, es-MX, es-AR, es-CO, etc.
• Chinese: zh-CN, zh-TW, zh-HK

Microsoft Azure Speech

• Custom model training for specific dialects
• Good support for major regional variants
• Fine-tuning capabilities

• Requires custom training for rare dialects
• More complex setup
• Higher cost for custom models

Dialect Transcription Accuracy by Model

English Dialects

Non-English Dialects

Challenges in Dialect Transcription

1. Phonetic Differences

• Standard: "house" /haʊs/
• Scottish: /hʊs/ or /hɯs/

• Use models trained on diverse data
• Larger models handle phonetic variations better
• May require post-processing

2. Vocabulary Differences

• Scottish: "wee" (small), "ken" (know), "bairn" (child)
• American Southern: "y'all" (you all), "fixin' to" (about to)

• Custom vocabulary lists
• Context-aware models
• Manual correction may be needed

3. Grammar Variations

• "He be working" (habitual aspect)
• "I ain't got none" (double negative)

• Models that understand context
• Accept grammatical variations
• Post-processing for standardization (if needed)

4. Code-Switching

• Spanglish (Spanish + English)
• Hinglish (Hindi + English)
• Singlish (Singapore English)

• Multilingual models (like Whisper)
• Models trained on code-switched data
• Language detection per segment

Strategies for Improving Dialect Transcription

1. Choose the Right Model

• Use standard models (Whisper, Google)
• Select appropriate language variant if available
• Larger models generally perform better

• Consider custom model training
• Use multilingual models
• May need to accept lower accuracy

2. Audio Quality Matters

• Clear, high-quality recordings
• Minimal background noise
• Good microphone placement
• Appropriate sample rate (16kHz minimum)

• Dialectal features are often subtle
• Poor audio masks important phonetic details
• Noise reduction can help

3. Provide Context

• Specify the dialect or region
• Provide sample text in the dialect
• Include vocabulary lists
• Use language/dialect selection if available

4. Use Larger Models

• Tiny/Base: Limited dialect support
• Small/Medium: Better dialect handling
• Large: Best dialect recognition

import whisper

# For dialect transcription, use larger models
model = whisper.load_model("large")  # Best for dialects
# or
model = whisper.load_model("medium")  # Good balance

result = model.transcribe("dialect_audio.wav")

5. Post-Processing

• Review transcriptions carefully
• Correct dialectal words
• Preserve dialectal features if desired
• Standardize if needed for your use case

# Example: Replace common dialectal words
dialect_replacements = {
    "gaun": "going",
    "ken": "know",
    "bairn": "child",
    # Add more as needed
}

def post_process_dialect(text, replacements):
    for dialect_word, standard_word in replacements.items():
        text = text.replace(dialect_word, standard_word)
    return text

Real-World Examples

Example 1: Scottish English

• ✅ Correctly understood meaning
• ❌ Standardized dialectal words ("gaun" → "going", "tae" → "to")
• ❌ May miss "messages" (Scottish for "groceries")

Example 2: Indian English

• ✅ Handles Indian English expressions well
• ✅ Recognizes "revert back" (common in Indian English)
• ✅ Good accuracy for major Indian English features

Example 3: African American Vernacular English (AAVE)

• ✅ Recognizes habitual "be"
• ✅ Handles AAVE grammar patterns
• ✅ Preserves dialectal features

Testing Dialect Transcription

How to Test Your Model

import whisper
import soundfile as sf

def test_dialect_transcription(audio_path, expected_text=None):
    """Test dialect transcription accuracy."""
    
    # Load model
    model = whisper.load_model("large")
    
    # Transcribe
    result = model.transcribe(audio_path)
    transcription = result["text"]
    
    print(f"Transcription: {transcription}")
    print(f"Language detected: {result['language']}")
    
    if expected_text:
        # Simple word error rate (WER) calculation
        expected_words = expected_text.lower().split()
        transcribed_words = transcription.lower().split()
        
        # Calculate accuracy (simplified)
        matches = sum(1 for w in expected_words if w in transcribed_words)
        accuracy = matches / len(expected_words) * 100
        
        print(f"Estimated accuracy: {accuracy:.1f}%")
    
    return transcription

# Test with your dialect audio
test_dialect_transcription("dialect_sample.wav")

Benchmarking Different Models

def compare_models_for_dialect(audio_path, models=["base", "small", "medium", "large"]):
    """Compare different model sizes for dialect transcription."""
    
    results = {}
    
    for model_name in models:
        print(f"\nTesting {model_name} model...")
        model = whisper.load_model(model_name)
        result = model.transcribe(audio_path)
        results[model_name] = {
            "text": result["text"],
            "language": result["language"],
            "segments": len(result["segments"])
        }
    
    # Compare results
    print("\n=== Comparison ===")
    for model_name, result in results.items():
        print(f"\n{model_name}:")
        print(f"  Text: {result['text'][:100]}...")
        print(f"  Language: {result['language']}")
    
    return results

# Compare models
compare_models_for_dialect("dialect_audio.wav")

Best Practices for Dialect Transcription

1. Know Your Dialect

• Research the specific dialect features
• Understand vocabulary differences
• Know phonetic variations
• Be aware of grammar differences

2. Set Realistic Expectations

• Not all dialects will transcribe perfectly
• Some standardization may occur
• Manual correction may be necessary
• Accuracy varies significantly by dialect

3. Use Appropriate Tools

• Choose models with good dialect support
• Use larger models when possible
• Consider custom training for specific dialects
• Test multiple models

4. Optimize Audio

• Record in quiet environments
• Use good microphones
• Ensure clear speech
• Minimize background noise

5. Post-Process When Needed

• Review transcriptions carefully
• Correct dialectal words
• Preserve or standardize based on use case
• Build custom vocabulary lists

Limitations and Considerations

Current Limitations

1. Rare Dialects
   • Limited or no training data
   • May require custom model training
   • Lower accuracy expected
2. Heavy Dialectal Features
   • Very regional speech may be challenging
   • Some features may be lost
   • Standardization may occur
3. Mixed Dialects
   • Code-switching adds complexity
   • Multiple dialects in one recording
   • Requires advanced models
4. Vocabulary Gaps
   • Dialectal words may not be recognized
   • Slang and colloquialisms
   • Regional expressions

When to Use Standard vs. Dialect Transcription

• You need standardized output
• Dialectal features aren't important
• Working with formal content
• Need consistency across speakers

• Dialectal features are meaningful
• Cultural authenticity matters
• Research or linguistic purposes
• Preserving speaker identity

Future of Dialect Transcription

Emerging Trends

1. Better Training Data
   • More diverse dialectal data
   • Regional dataset collection
   • Community contributions
2. Custom Model Training
   • Easier fine-tuning
   • Dialect-specific models
   • Transfer learning
3. Multilingual Models
   • Better code-switching
   • Cross-dialect understanding
   • Unified models
4. Real-Time Adaptation
   • Learning from corrections
   • User-specific adaptation
   • Context-aware transcription

Conclusion

• Major regional variants (British, Australian, Indian English)
• Common accents and pronunciation differences
• Well-represented dialects in training data

• Rare or very regional dialects
• Heavy dialectal features
• Uncommon vocabulary
• Mixed dialects and code-switching

1. Use larger, well-trained models (Whisper large, Google STT)
2. Optimize audio quality
3. Set realistic expectations
4. Post-process when necessary
5. Consider custom training for specific needs

Additional Resources

• Whisper for Multilingual Transcription
• How to Improve Speech-to-Text Accuracy
• Speech-to-Text for Beginners