Segmental Neural Net Optimization for Continuous Speech Recognition

Previously, we had developed the concept of a Segmental Neural Net (SNN) for phonetic modeling in continuous speech recognition (CSR). This kind of neu-ral network technology advanced the state-of-the-art of large-vocabulary CSR, which employs Hidden Marlcov Models (HMM), for the ARPA 1oo0-word Re-source Management corpus. More Recently, we started porting the neural net system to a larger, more challenging corpus- the ARPA 20,Ooo-word Wall Street Journal (WSJ) corpus. During the porting, we explored the following research directions to refine the system: i) training context-dependent models with a reg-ularization method; ii) training SNN with projection pursuit; and ii) combining different models into a hybrid system. When tested on both a development set and an independent test set, the resulting neural net system alone yielded a per-fonnance at the level of the HMM system, and the hybrid SNN/HMM system achieved a consistent 10-15 % word error reduction over the HMM system. This paper describes our hybrid system, with emphasis on the optimization methods employed.