Abstract
There is a great amount of information in the speech signal, although current speech recognizers do not exploit it completely. In this paper articulatory information is extracted from speech and fused to standard acoustic models to obtain a better hybrid acoustic model which provides improvements on speech recognition. The paper also studies the best input signal for the system in terms of type of speech features and time resolution to obtain a better articulatory information extractor. Then this information is fused to a standard acoustic model obtained with neural networks to perform the speech recognition achieving better results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
