An HMM based speaker-independent continuous speech recognition system with experiments on the TIMIT database

The authors recently designed and implemented a large-vocabulary, speaker-independent, continuous speech recognition system. The system is based on hidden Markov modeling (HMM) of phoneme-sized acoustic units using continuous mixture Gaussian densities. The main structure of the system is outlined with a focus on a method of generating mixture Gaussian density models through a merging procedure whose efficiency was recently improved significantly. The system has been evaluated on the TIMIT database on a task of vocabulary size 853 and various grammar perplexities. The word accuracies are 92.2%, 84.9%, and 60.1% for the test set perplexities of 25, 106, and 853 (no grammar), respectively