Locally constrained dynamic programming in automatic speech recognition

Recent years have seen the emergence of dynamic programming as one of the most important tools available for overcoming temporal variability problems in automatic speech recognition. Considerable research effort has been devoted to the investigation of different dynamic programming algorithms, yet most work has been concerned with global, rather than local, constraints upon temporal variation. Clearly the likelihood of timescale distortion is not constant for the entire duration of an utterance; variability must be, at least to some extent, data dependent. It is therefore desirable that information to this effect should be made available to the dynamic time warping process. This paper describes a technique for obtaining an estimate of local timescale variability based on a bi-directional dynamic programming algorithm and basic fuzzy set theory. Results are presented which indicate that this technique will lead to improved discrimination, especially if the differences between classes are mainly due to temporal structure.