Detection of speech over the telephone network - Valentus, Vincent Peter C. (1997)
This thesis seeks to address the problem of detecting the presence of human speech in a short-time segment of a telephone line signal. It consists of the design, implementation, and evaluation of a statistical pattern classifier based on a neural network paradigm to identify speech over a background of silence and other non-speech signals, such as the tones used for the telephone service. The designed algorithm for speech detection is based on the method proposed by J. Hoyt and D. Wechsler [15] with modifications to process a more suitable set of signal feature measurements. The classifier uses a Radial Basis Function networks to discriminate between speech and non-speech signals. This thesis experimented on two different basis functions for the hidden layer nodes of the network to determine the effect of using the Mahalanobis and Euclidean distance on the performance of the designed classifier. This thesis also examined two different network structures in the desire to improve the reliability of the algorithm to make the two-class decision across a variety of telephone connections. The practicability of the designed speech detectors is demonstrated by an actual implementation of the algorithm in Matlab. The performance of the detectors is measured using the percentage of correct classification as the basis for evaluation. Results show that a reliable speech vs. not-speech decision, with an accuracy of 99% can be achieved by using an RBF network with a single output neuron, which uses the Mahalanobis distance in the calculation of the basis functions, and which uses the features that were derived from 64 msec. frames of signals.