Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: There have been higher demands recently for Automatic Speech Recognition (ASR) systems able to operate robustly in acoustically noisy environments.  proposes a method to effectively integrate audio and visual information in audio-visual (bi-modal) ASR systems. Such integration inevitably necessitates modeling of the synchronization of the audio and visual information. To address the time lag and correlation problems in individual features beten speech and lip movements,  introduce a type of integrated HMM modeling of audio-visual information based on a family of HMM composition. The proposed model can represent state synchronicity not only within a phoneme but also beten phonemes. Furthermore,  also propose a rapid stream ight optimization based on GPD algorithm for noisy bi-modal speech recognition. Evaluation experiments show that the proposed method improves the recognition accuracy for noisy speech. In SNR=0dB our proposed method attained 16% higher performance compared to a product HMMs without the synchronicity re-estimation.
['Satoshi Nakamura', "Ken'ichi Kumatani", 'Satoshi Tamura']
I am writing a research paper on a idea Robust bi-modal speech recognition based on state synchronous modeling and stream weight optimization
[{"from": "human", "value": "I am writing a research paper on a idea Robust bi-modal speech recognition based on state synchronous modeling and stream weight optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: There have been higher demands recently for Automatic Speech Recognition (ASR) systems able to operate robustly in acoustically noisy environments.  proposes a method to effectively integrate audio and visual information in audio-visual (bi-modal) ASR systems. Such integration inevitably necessitates modeling of the synchronization of the audio and visual information. To address the time lag and correlation problems in individual features beten speech and lip movements,  introduce a type of integrated HMM modeling of audio-visual information based on a family of HMM composition. The proposed model can represent state synchronicity not only within a phoneme but also beten phonemes. Furthermore,  also propose a rapid stream ight optimization based on GPD algorithm for noisy bi-modal speech recognition. Evaluation experiments show that the proposed method improves the recognition accuracy for noisy speech. In SNR=0dB our proposed method attained 16% higher performance compared to a product HMMs without the synchronicity re-estimation."}]