Hidden-Articulator Markov Models for Speech Recognition

Speech Communication | , Vol 41: pp. 511-529

Most existing automatic speech recognition systems today do not explicitly use knowledge about human speech production. We show that the incorporation of articulatory knowledge into these systems is a promising direction for speech recognition, with the potential for lower error rates and more robust performance. To this end, we introduce the Hidden-Articulator Markov Model (HAMM), a model which directly integrates articulatory information into speech recognition. The HAMM is an extension of the articulatory-feature model introduced by Erler in 1996. We extend the model by using diphone units, developing a new technique for model initialization, and constructing a novel articulatory feature mapping. We also introduce a method to decrease the number of parameters, making the HAMM comparable in size to standard HMMs. We demonstrate that the HAMM can reasonably predict the movement of articulators, which results in a decreased word error rate. The articulatory knowledge also proves useful in noisy acoustic conditions. When combined with a standard model, the HAMM reduces word error rate 28-35% relative to the standard model alone