Voiced/nonvoiced detection in compressively sensed speech signals

We leverage the recent algorithmic advances in compressive sensing (CS), and propose a novel unsupervised voiced/nonvoiced (V/NV) detection method for compressively sensed speech signals. It attempts to exploit the fact that there is significant glottal activity during production of voiced speech while the same is not true for nonvoiced speech. This characteristic of the speech production mechanism is captured in the sparse feature vector derived using CS framework. Further, we propose an information theoretic metric, for V/NV classification, exploiting the sparsity of the extracted feature using a signal adaptive dictionary motivated by speech production mechanism. The final classification is done using an adaptive threshold selection scheme, which uses the temporal information of speech signals. While existing methods of feature extraction use speech samples directly, proposed method performs V/NV detection in compressively sensed speech signals (requiring very less memory), where existing time or frequency domain detection methods are not directly applicable. Hence, this method can be effective for various speech applications. Performance of the proposed method is studied on CMU-ARCTIC database, for eight types of additive noises, taken from the NOISEX database, at different signal-to-noise ratios (SNRs). The proposed method performs similar or better compared to the existing methods, especially at lower SNRs and this provide compelling evidence of the effectiveness of sparse feature vector for V/NV detection.