K. Sri Rama Murty

Speaker-specific information from residual phase

This paper demonstrates the presence of speaker-specific information in the residual phase using autoassociative neural network (AANN) models. The residual phase is extracted from the speech signal after eliminating the vocal tract information by the linear prediction (LP) analysis. AANN models are used for capturing the speaker-specific information present in the residual phase. The speaker recognition studies infer that the residual phase contains significant speaker-specific information and it is indeed captured by the AANN models. In this study we also demonstrate that in voiced speech segments, regions around the instants of glottal closure are more speaker-specific compared to other regions.

Prosody Modification Using Allpass Residual of Speech Signals.

In this paper, we attempt to signify the role of phase spectrum of speech signals in acquiring an accurate estimate of excitation source for prosody modification. The phase spectrum is parametrically modeled as the response of an all pass (AP) filter, and the filter coefficients are estimated by considering the linear prediction (LP) residual as the output of the AP filter. The resultant residual signal, namely AP residual, exhibits unambiguous peaks corresponding to epochs, which are chosen as pitch markers for prosody modification. This strategy efficiently removes ambiguities associated with pitch marking, required for pitch synchronous overlap-add (PSOLA) method. The prosody modification using AP residual is advantageous than time domain PSOLA (TD-PSOLA) using speech signals, as it offers fewer distortions due to its flat magnitude spectrum. Windowing centered around unambiguous peaks in AP residual is used for segmentation, followed by pitch/duration modification of AP residual by mapping of pitch markers. The modified speech signal is obtained from modified AP residual using synthesis filters. The mean opinion scores are used for performance evaluation of the proposed method, and it is observed that the AP residual-based method delivers equivalent performance as that of LP residual based method using epochs, and better performance than the linear prediction PSOLA (LP-PSOLA).