The discriminative network: A mechanism for focusing recognition in whole-word pattern matching

Whole-word pattern matching using dynamic time-warping (DTW) has achieved considerable success as an algorithm for automatic speech recognition. However, the performance of such an algorithm is ultimately limited by its inability to discriminate between similar sounding words. The problem arises because all differences between speech patterns are treated as being equally important, hence the algorithm is particularly susceptible to confusions caused by irrelevant differences. This paper presents an alternative DTW approach which is able to focus its attention on those parts of a speech pattern which serve to distinguish it from similar patterns. A network-type data structure is derived from reference speech patterns, and the separate paths through the network determine the regions where recognition takes place. Results indicate that discrimination between similar sounding words can be greatly improved.