Detection of human speech using hybrid recognition models

This paper describes the research to develop an efficient system that provides a binary decision as to the presence of speech in a short time sample of an acoustic signal. A method which is efficient and reliably detects human speech in the presence of structured noise (such as wind, music, traffic sounds, etc.) is described. There are methods which work well to detect speech in a communications environment, but previous methods can not distinguish speech from a quasi-periodic signal that have a spectral power density similar to speech (such as music). Two separate feature sets are evaluated, reliable detection is obtained down to signal to noise ratios (SNR) as low as 0 dB. The algorithm utilized is a statistical pattern classifier with radial basis function networks. Mel-cepstra and wavelet feature vectors are compared. A method of obtaining the temporal feature information is also described.