Tied mixture continuous parameter models for large vocabulary isolated speech recognition

Abstract

The acoustic modeling problem in automatic speech recognition is examined with the specific goal of unifying discrete and continuous parameter approaches. The authors consider a class of very general hidden Markov models, which can accommodate sequences of information-bearing acoustic feature vectors lying either in a discrete or in a continuous space. More generally, 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 a large (5000-word) vocabulary office correspondence task, demonstrate some of the benefits associated with this technique.