Comparative Study of the Baum-Welch and Viterbi Training Algorithms Applied to Read and Spontaneous Speech Recognition

In this paper we compare the performance of acoustic HMMs obtained through Viterbi training with that of acoustic HMMs obtained through the Baum-Welch algorithm. We present recognition results for discrete and continuous HMMs, for read and spontaneous speech databases, acquired at 8 and 16 kHz. We also present results for a combination of Viterbi and Baum-Welch training, intended as a trade-off solution. Though Viterbi training yields a good performance in most cases, sometimes it leads to suboptimal models, specially when using discrete HMMs to model spontaneous speech. In these cases, Baum-Welch shows more robust than both Viterbi training and the combined approach, compensating for its high computational cost. The proposed combination of Viterbi and Baum-Welch only outperforms Viterbi training in the case of read speech at 8 kHz. Finally, when using continuous HMMs, Viterbi training reveals as good as Baum-Welch at a much lower cost.