A probabilistic framework for segment-based speech recognition

Most current speech recognizers use an observation space based on a temporal sequence of measurements extracted from fixed-length "frames" (e.g., Mel-cepstra). Given a hypothetical word or sub-word sequence, the acoustic likelihood computation always involves all observation frames, though the mapping between individual frames and internal recognizer states will depend on the hypothesized segmentation. There is another type of recognizer whose observation space is better represented as a network, or graph, where each arc in the graph corresponds to a hypothesized variable-length segment that is represented by a fixed-dimensional "feature". In such feature-based recognizers, each hypothesized segmentation will correspond to a segment sequence, or path, through the overall segment-graph that is associated with a subset of all possible feature vectors in the total observation space. In this work we examine a maximum a posteriori decoding strategy for feature-based recognizers and develop a normalization criterion useful for a segment-based Viterbi or A* search. Experiments are reported for both phonetic, and word recognition tasks. (C) 2003 Elsevier Science Ltd. All rights reserved.