This paper shows an effective speech/pause discrimination method combining spectral noise filtering and fuzzy logic rules. The fuzzy system is based on a Sugeno inference engine with membership functions defined as combination of two Gaussian functions. Its operation is optimized by means of a hybrid training algorithm combining the least-squares method and the backpropagation gradient descent method for training membership function parameters. The fuzzy classifier consists of ten fuzzy rules defined in terms of the denoised subband signal-to-noise ratios (SNRs) and the zero crossing rate (ZCRs). An exhaustive analysis conducted on the Spanish Speech Dat-Car databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs and a representative set of recently reported VAD algorithms.
['Rafael Culebras', 'Javier Ramírez 0001', 'Juan Manuel Górriz']
Effective Speech/Pause Discrimination Combining Noise Suppression and Fuzzy Logic Rules