Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The performance of automatic speech recognition (ASR) system can be significantly enhanced with additional information from visual speech elements such as the movement of lips, tongue, and teeth, especially under noisy environment. , a novel approach for recognition of visual speech elements is presented. The approach makes use of adaptive boosting (AdaBoost) and hidden Markov models (HMMs) to build an AdaBoost-HMM classifier. The composite HMMs of the AdaBoost-HMM classifier are trained to cover different groups of training samples using the AdaBoost technique and the biased Baum-lch training method. By combining the decisions of the component classifiers of the composite HMMs according to a novel probability synthesis rule, a more complex decision boundary is formulated than using the single HMM classifier. The method is applied to the recognition of the basic visual speech elements. Experimental results show that the AdaBoost-HMM classifier outperforms the traditional HMM classifier in accuracy, especially for visemes extracted from contexts.
['Say Wei Foo', 'Yong Lian', 'Liang Dong']
I am writing a research paper on a idea Recognition of visual speech elements using adaptively boosted hidden Markov models
[{"from": "human", "value": "I am writing a research paper on a idea Recognition of visual speech elements using adaptively boosted hidden Markov models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The performance of automatic speech recognition (ASR) system can be significantly enhanced with additional information from visual speech elements such as the movement of lips, tongue, and teeth, especially under noisy environment. , a novel approach for recognition of visual speech elements is presented. The approach makes use of adaptive boosting (AdaBoost) and hidden Markov models (HMMs) to build an AdaBoost-HMM classifier. The composite HMMs of the AdaBoost-HMM classifier are trained to cover different groups of training samples using the AdaBoost technique and the biased Baum-lch training method. By combining the decisions of the component classifiers of the composite HMMs according to a novel probability synthesis rule, a more complex decision boundary is formulated than using the single HMM classifier. The method is applied to the recognition of the basic visual speech elements. Experimental results show that the AdaBoost-HMM classifier outperforms the traditional HMM classifier in accuracy, especially for visemes extracted from contexts."}]