Two-channel speech analysis

We present a two-channel technique for improving speech analysis in certain applications. One channel is the signal from an electroglottograph (EGG), which monitors the vibratory motion of the vocal folds. The other channel is the speech signal obtained from a conventional microphone. We show how the EGG can be used as a tool for validating speech processing algorithms and estimating possible lower bounds for both computation and performance of these algorithms, particularly closed-phase speech analysis. Our system is used to classify speech segments as voiced, unvoiced, mixed voiced, and silent and to estimate the fundamental frequency of voicing. This four-way classification is not implemented as a complete algorithm; it still requires some user judgments and decisions. The technical results, however, illustrate an EGG-based algorithm for voiced/unvoiced-silent classification. In addition, we illustrate how automatic on-line inverse filtering can be achieved. The results demonstrate the superiority of the closed-phase covariance analysis method over several other commonly used methods. Source-tract coupling is shown to be a significant factor in linear prediction analysis, a factor commonly ignored to date. Various applications of our two-channel approach are described along with the major disadvantage, namely, that in some situations the EGG channel cannot be acquired.