Tied mixture continuous parameter modeling for speech recognition

The acoustic-modeling problem in automatic speech recognition is examined with the goal of unifying discrete and continuous parameter approaches. To model a sequence of information-bearing acoustic feature vectors which has been extracted from the speech waveform via some appropriate front-end signal processing, a speech recognizer basically faces two alternatives: (1) assign a multivariate probability distribution directly to the stream of vectors, or (2) use a time-synchronous labeling acoustic processor to perform vector quantization on this stream, and assign a multinomial probability distribution to the output of the vector quantizer. With a few exceptions, these two methods have traditionally been given separate treatment. A class of very general hidden Markov models which can accommodate feature vector sequences lying either in a discrete or in a continuous space is considered; the new class allows one to represent the prototypes in an assumption-limited, yet convenient way, as tied mixtures of simple multivariate densities. Speech recognition experiments, reported for two (5000- and 20000-word vocabulary) office correspondence tasks, demonstrate some of the benefits associated with this technique