Wav2Vec2_CommonPhone
| Property | Value |
|---|---|
| Parameter Count | 316M parameters |
| Model Type | Wav2Vec2 Large with Common Phone |
| License | CC0-1.0 |
| Languages | English, German, French, Spanish, Russian, Italian |
| Author | Philipp Klumpp |
| GitHub Repository | https://github.com/PKlumpp/phd_model |
What is Wav2Vec2_CommonPhone?
Wav2Vec2_CommonPhone is a sophisticated multilingual phone recognition model designed specifically for analyzing pathological speech signals. Developed as part of a PhD thesis, this model represents a significant advancement in acoustic modeling, utilizing the Wav2Vec2 architecture with a linear projection to handle CTC blank token and 101 phone symbols from the International Phonetic Alphabet (IPA).
Implementation Details
The model processes 16 kHz audio input to predict IPA phone sequences, leveraging the Common Phone dataset which includes over 11,000 speakers from Mozilla's Common Voice dataset. It's built upon the Wav2Vec2 XLSR-53 architecture and demonstrates impressive phone error rates across all supported languages, with an average PER of 9.2%.
- Multilingual support for 6 major languages
- 316M parameters for robust acoustic modeling
- Specialized for pathological speech analysis
- Uses Common Phone dataset with 11,000+ speakers
Core Capabilities
- Phone recognition with IPA symbols
- Multilingual processing capability
- High accuracy across all supported languages
- Optimized for pathological speech analysis
- Real-time phone sequence prediction
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on pathological speech analysis and impressive multilingual capabilities, achieving remarkably low phone error rates across six languages. Its training on the Common Phone dataset with over 11,000 speakers ensures robust performance in varied acoustic conditions.
Q: What are the recommended use cases?
The model is particularly suited for: pathological speech analysis, multilingual phone recognition tasks, speech research applications, and acoustic modeling scenarios requiring IPA phone sequence prediction. It's especially valuable for researchers and practitioners working with speech disorders or multilingual speech analysis.
