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An overlapping-feature-based phonological model incorporating linguistic constraints: applications to speech
1University of Waterloo, Ontario, Canada.
The Journal of the Acoustical Society of America
|February 28, 2002
Summary
This study introduces an overlapping-feature phonological model for speech recognition. This model improves accuracy by incorporating linguistic constraints and long-span contextual dependencies, outperforming traditional methods.
Area of Science:
- Computational Linguistics
- Speech Recognition Technology
- Acoustic Phonetics
Background:
- Modeling phonological units is crucial for accurate speech recognition.
- Existing models often struggle with long-span contextual dependencies in speech acoustics.
Purpose of the Study:
- To develop and present an overlapping-feature-based phonological model.
- To incorporate high-level linguistic constraints for improved speech acoustic modeling.
Main Methods:
- Developed an overlapping-feature model integrating linguistic information like word/phrase boundaries, morphemes, syllables, and stress.
- Utilized a consistent computational framework for model construction.
- Incorporated hidden Markov model (HMM) states based on feature-overlapping patterns.
Main Results:
- The overlapping-feature model demonstrated improved performance in an HMM-based speech recognition system.
- Achieved better results compared to the conventional triphone-based phonological model.
Conclusions:
- The proposed overlapping-feature model effectively captures long-span contextual dependencies.
- This approach offers a significant advancement over traditional phonological models in speech recognition.
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