Intel Corporation

Yibao Zhao

Ara Victor Nefian

Yonghong Yan

Xiaobo Pi

Luhong Liang

Zhiwei Lin

Xiaoxing Liu

A speech recognition method includes several embodiments describing application of support vector machine analysis to a mouth region. Lip position can be accurately determined and used in conjunction with synchronous or asynchronous audio data to enhance speech recognition probabilities.

Audio-visual feature fusion and support vector machine useful for continuous speech recognition

Method and apparatus for an audiovisual continuous speech recognition (AVCSR) system using a coupled hidden Markov model (CHMM) are described herein. In one aspect, an exemplary process includes receiving an audio data stream and a video data stream, and performing continuous speech recognition based on the audio and video data streams using a plurality of hidden Markov models (HMMs), a node of each of the HMMs at a time slot being subject to one or more nodes of related HMMs at a preceding time slot. Other methods and apparatuses are also described.

Coupled hidden Markov model (CHMM) for continuous audiovisual speech recognition

A method and system providing a statistical representation from rule-based grammar specifications. The language model is generated by obtaining a statistical representation of a rule-based language model and combining it with a statistical representation of a statistical language model for use as a final language model. The language model may be enhanced by applying smoothing and/or adapting for use as the final language model.

Method and system for building a domain specific statistical language model from rule based grammar specifications

A method and system for providing a class-based statistical language model representation from rule-based knowledge is disclosed. The class-based language model is generated from a statistical representation of a class-based rule net. A class-based rule net is generated using the domain-related rules with words replaced with their corresponding class-tags that are manually defined. The class-based statistical representation from the class-based rule net is combined with a class-based statistical representation from a statistical language model to generate a language model. The language model is enhanced by smoothing/adapting with general-purpose and/or domain-related corpus for use as the final language model. A two-pass search algorithm is applied for speech decoding.

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Beijing, China

Yibao Zhao