We propose a new method of processing speech degraded by reverberation. The method is based on analysis of short (2 ms) segments of data to enhance the regions in the speech signal having a high signal-to-reverberant component ratio (SRR). The short segment analysis shows that SRR is different in different segments of speech. The processing method involves identifying and manipulating the linear prediction residual signal in three different regions of the speech signal, namely, high SRR region, low SRR region, and only reverberation component region. A weight function is derived to modify the linear prediction residual signal. The weighted residual signal samples are used to excite a time-varying all-pole filter to obtain perceptually enhanced speech. The method is robust to noise present in the recorded speech signal. The performance is illustrated through spectrograms, subjective and objective evaluations.

We study the robustness of a group-delay-based method for determining the instants of significant excitation in speech signals. These instants correspond to the instants of glottal closure for voiced speech. The method uses the properties of the global phase characteristics of minimum phase signals. Robustness of the method against noise and distortion is due to the fact that the average phase characteristics of a signal is determined mainly by the strength of the excitation impulse. The strength of excitation is determined by the energy of the residual error signal around the instant of excitation. We propose a measure for the strength of the excitation based on Frobenius norm of the differenced signal. The robustness of the group-delay-based method is illustrated for speech under different types of degradations and for speech from different speakers.

In this paper we propose a new method of processing speech degraded by reverberation. The method is based on analysis of short (2 ms) segments of data to enhance the regions in the speech signal having high signal to reverberant component ratio (SRR). The short segment analysis shows that SRR is different in different segments of speech. The processing method involves identifying and manipulating the linear prediction residual in three different regions of the speech signal, namely, the high SRR region, the low SRR region and only the reverberation component region. A weighting function is derived to modify the LP residual. The weighted residual samples are used to excite the time-varying LP all-pole filter to obtain perceptually enhanced speech.

In this paper we propose a method for enhancement of speech in the presence of additive noise. The objective is to selectively enhance the high SNR regions in the noisy speech in the temporal and spectral domains, without causing significant distortion in the resulting enhanced speech. This is proposed to be done at three different levels: (a) At the gross level, by identifying the regions of speech and noise in the temporal domain, (b) At the finer level, by identifying the regions of high and low SNR portions in the noisy speech, and (c) At the short--time spectrum level, by enhancing the spectral peaks over spectral valleys. Processing of noisy speech for enhancement involves mostly weighting the LP residual samples. The weighted residual samples are used to excite the time-- varying LP filter to produce enhanced speech. 1. INTRODUCTION Speech signal collected under normal environmental conditions is usually degraded due to noise and distortions. Performance of speech systems depe...

A new method of analysis of speech is proposed that will bring out variations in vocal tract system characteristics in short (2-4 ms) segments. In this method, the source and system components of the speech signal are suitably windowed to reduce the effects of truncation of conventional waveform windowing.