Hybrid system combining expert-TDNNs and HMMs for continuous speech recognition

Hybrid systems, using neural networks (NNs) and hidden Markov models (HMMs) are designed to take advantage of both methods; the pattern classification power of NNs and the temporal modelling structure of HMMs. This paper describes the use of expert sub-network modules of the type time delay neural network (TDNN) for phone recognition of continuous speech. The originality of the hybrid system developed is in combining the probabilities of the modular TDNN architecture with those of CDHMMs during the recognition phase. On three speakers of the DARPA RM speaker-dependent task, we show that these small TDNNs trained on phone ambiguities can improve word recognition performance of state-of-the-art CDHMMs. The TDNN implementation achieved a word error rate reduction of 15%. We discuss strategies for extending this approach from the DARPA RM speaker-dependent database to the larger DARPA RM speaker-independent database.<
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