Abstract
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 (WER). The articulatory knowledge also proves useful in noisy acoustic conditions. When combined with a standard model, the HAMM reduces WER 28–35% relative to the standard model alone.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.