Title :
Significance of the Modified Group Delay Feature in Speech Recognition
Author :
Hegde, Rajesh M. ; Murthy, Hema A. ; Gadde, Venkata Ramana Rao
Author_Institution :
Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Madras, Chennai
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
Keywords :
Fourier transforms; feature extraction; speaker recognition; speech processing; Fourier transform magnitude; cepstral features; continuous-speech recognition; features extraction; group delay function; language recognition; modified group delay feature; phase spectra; phase spectrum; pitch periodicity effects; speaker recognition; speech perception; speech spectral representation; Data mining; Delay effects; Feature extraction; Fourier transforms; Resonance; Signal processing; Speech coding; Speech processing; Speech recognition; Wrapping; Class separability; Gaussian mixture models (GMMs); feature extraction; feature selection; group delay function; hidden Markov models (HMMs); phase spectrum; robustness;
Journal_Title :
Audio, Speech, and Language Processing, IEEE Transactions on
DOI :
10.1109/TASL.2006.876858