On the use of energy in LPC‐based recognition of isolated words
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Recognition of isolated words by encoding speech into Linear Predictive Coefficients (LPC) is well known and widely accepted as one of the better methods for speech recognition. One of the drawbacks in relying entirely on LPC for recognition, however, is that the energy information in the speech is removed during the LPC analysis. This talk discusses problems involved in combining energy pattern information with the LPC pattern information and presents results of recognition experiments with one method. The method is general and applicable to the addition of any new feature to the recognition feature set. The energy information and LPC information are combined linearly in the frame‐by‐frame manner utilizing the dynamic time warping (DTW) method of Itakura. The Itakura distance function, which determines the spectral difference between two frames of speech, does not lend itself to direct statistical analysis in multiple dimensions. The method for obtaining the weighting for the linear combination involves ...