Incorporation of time-varying LP cepstral features in HMM-based isolated word speech recognition

Current state-of-the-art automatic, continuous speech recognition systems have enjoyed huge leaps in accuracy using speech features that assumes stationarity in the signals that are being processed. However, the said performance can often be attributed to the inclusion of lexical information. For the case of isolated word tasks, without the use of a priori models for the expected words, the static speech representation breaks down. For example, words that only differ in one unvoiced part are often misrecognized. Thus, time-varying speech representations has become an interest in the field. This paper is concerned with the use of simple time-varying features based on an autoregressive modeling of speech that provides high resolution features. In particular, how the said high resolution features fit into a finite-length Hidden Markov Model-based acoustic model that was originally used for static features. Its performance is compared with the best performing static features (Mel-Frequency Cepstral Coefficients) and while it is currently viewed as suboptimal, ample rooms for improvement are also emphasized.