Rajesh M. Hegde

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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(More)
Feature extraction and selection for continuous speech recognition is a complex task. State of the art speech recognition systems use features that are derived by ignoring the Fourier transform phase. In our earlier studies we have shown the efficacy of The Modified Group Delay Feature (MODGDF) derived from the Fourier transform phase for phoneme, syllable(More)
This paper investigates the significance of combining cepstral features derived from the modified group delay function and from the short-time spectral magnitude like the MFCC. The conventional group delay function fails to capture the resonant structure and the dynamic range of the speech spectrum primarily due to pitch periodicity effects. The group delay(More)
In the development of a syllable-centric ASR system, segmentation of the acoustic signal into syllabic units is an important stage. This paper presents a minimum phase group delay based approach to segment spontaneous speech into syllable-like units. Here, three different minimum phase signals are derived from the short term energy functions of three(More)
Speakers are generally identified by using features derived from the Fourier transform magnitude. The Modified group delay feature(MODGDF) derived from the Fourier transform phase has been used effectively for speaker recognition in our previous efforts.Although the efficacy of the MODGDF as an alternative to the MFCC is yet to be established, it has been(More)
Conventionally the spectral magnitude of MUSIC is used for efficient beam forming and clean speech acquisition from distant microphones. The MUSIC method is unable to resolve closely spaced DOAs with a computationally plausible number of sensors. In this paper we propose the use of the group delay function computed from theMUSIC phase spectrum for efficient(More)
Spherical harmonics root-MUSIC (MUltiple SIgnal Classification) technique for source localization using spherical microphone array is presented in this paper. Earlier work on root-MUSIC is limited to linear and planar arrays. Root-MUSIC for planar array utilizes the concept of manifold separation and beamspace transformation. In this paper, the Vandermonde(More)