In this paper we propose a method for extracting the fundamental frequency (fo) from degraded speech signals using single frequency filtering (SFF) approach. The SFF of frequency-shifted speech signal gives high signal-to-noise ratio (SNR) segments at some frequencies and hence the SFF approach can be exploited for foextraction using autocorrelation function of those segments. Since the fois computed from the envelope of a single frequency component of the signal, the vocal tract resonances do not affect the foextraction. The use of the high SNR frequency component in a given segment helps in overcoming the effects of degradations in the speech signal, without explicitly estimating the characteristics of noise. The proposed method of foextraction is shown to give better performance for several types of real and simulated degradations, in comparison with some of the methods reported recently in the literature.

In this paper, a signal processing approach is proposed for speech/nonspeech discrimination. The approach is based on single frequency filtering (SFF), where the amplitude envelope of the signal is obtained at each frequency with high temporal and spectral resolution. This high resolution property helps to exploit the resulting high signal-to-noise ratio (SNR) regions in time and frequency. The variance of the spectral information across frequency is higher for speech and lower for many types of noises. The mean and variance of the noise-compensated weighted envelopes are computed across frequency at each time instant. Decision logic is applied to the feature derived from the mean and variance values on varieties of degradations, including NTIMIT, CTIMIT and distance speech, besides degradation due to standard noise types. In all cases, the proposed method gives significantly better performance than the standard Adaptive Multi-rate VAD2 (AMR2) method. AMR2 method is chosen for comparison, as the method adapts itself for different degradations, and is seen to give good performance over different SNR situations. The proposed method does not use training data to derive the characteristics of speech or noise, nor makes any assumption on the nonspeech beginning. The SFF method appears promising in other applications of speech processing, such as pitch extraction and speech enhancement.