The Lincoln large-vocabulary stack-decoder HMM CSR

Recognition of the Wall Street Journal (WSJ) pilot database, a continuous-speech-recognition (CSR) database which supports 5 K, 20 K, and up to 64 K-word CSR tasks, is examined. The original Lincoln tied-mixture hidden Markov model (HMM) CSR was implemented using a time-synchronous beam-pruned search of a static network which does not extend well to this task because the recognition network would be too large. Therefore, the recognizer has been converted to a stack decoder-based search strategy. This decoder has been shown to function effectively on up to 64 K-word recognition of continuous speech. Recognition-time adaptation has also been added to the recognizer. The acoustic modeling techniques and the implementation of the stack decoder used to obtain these results are described.<>