Incorporating acoustic-phonetic knowledge in hybrid TDNN/HMM frameworks

A comparison of several architectures of time-delayed neural networks (TDNN) as the preprocessing step for hidden Markov modeling (HMM) speaker-dependent continuous-speech recognition systems is presented. A modular TDNN architecture on the basis of acoustic-phonetic knowledge, where each sub-network is trained on a different subset of phonemes is defined. It allows the authors to define a hierarchical tree structure of sub-networks. This structure offers the possibility of proposing a framework to enlarge the number of outputs by defining context-dependent sub-networks. They also compare different methods for integrating TDNN in a HMM framework, a discrete and a continuous integration. For the speaker JWS-4 of the speaker-dependent DARPA RM1 database, with context independent phonemes, 21.3% word error rate are obtained without grammar, 4.6% with the DARPA word-pair grammar (perplexity of 60)