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Significance of the modified group delay feature in speech recognition
- Source :
- IndraStra Global.
- Publication Year :
- 2007
-
Abstract
- Spectral representation of speech is complete when both the Fourier transform magnitude and phase spectra are specified. In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech perception has been realized, few attempts have been made to extract features from it. This is primarily because the resonances of the speech signal which manifest as transitions in the phase spectrum are completely masked by the wrapping of the phase spectrum. Hence, an alternative to processing the Fourier transform phase, for extracting speech features, is to process the group delay function which can be directly computed from the speech signal. The group delay function has been used in earlier efforts, to extract pitch and formant information from the speech signal. In all these efforts, no attempt was made to extract features from the speech signal and use them for speech recognition applications. This is primarily because the group delay function fails to capture the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects. In this paper, the group delay function is modified to overcome these effects. Cepstral features are extracted from the modified group delay function and are called the modified group delay feature (MODGDF). The MODGDF is used for three speech recognition tasks namely, speaker, language, and continuous-speech recognition. Based on the results of feature and performance evaluation, the significance of the MODGDF as a new feature for speech recognition is discussed. � 2006 IEEE.
- Subjects :
- Speech perception
Acoustics and Ultrasonics
Speech recognition
Spurious signal noise
Communication channels (information theory)
Wavelet transforms
Continuous speech recognition
Computational grammars
Cepstrum
Hidden Markov models
Electrical and Electronic Engineering
Gaussian mixture models (GMMs)
Robustness
Mathematics
Group delay and phase delay
Voice activity detection
business.industry
Hidden Markov models (HMMs)
Pattern recognition
Object recognition
Speech processing
Linear predictive coding
Speaker recognition
Phase spectrum
Fourier transforms
Formant
Phase transitions
Class separability
Feature selection
Feature extraction
Speech analysis
Trellis codes
Artificial intelligence
business
Group delay function
Subjects
Details
- Language :
- English
- ISSN :
- 23813652
- Database :
- OpenAIRE
- Journal :
- IndraStra Global
- Accession number :
- edsair.doi.dedup.....a8f67609da43f64ac4ca72cc59736ba8
- Full Text :
- https://doi.org/10.1109/TASL.2006.876858