Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a prototype for the automatic recognition of audio-visual speech, developed to augment the IBM ViaVoice/spl trade/ speech recognition system. Frontal face, full frame video is captured through a USB 2.0 interface by means of an inexpensive PC camera, and processed to obtain appearance-based visual features. Subsequently, these are combined with audio features, synchronously extracted from the acoustic signal, using a simple discriminant feature fusion technique. On the average, the required computations utilize approximately 67% of a Pentium/spl trade/ 4, 1.8 GHz processor, leaving the remaining resources available to hidden Markov model based speech recognition. Real-time performance is there- fore achieved for small-vocabulary tasks, such as connected-digit recognition. In the paper,  discuss the prototype architecture based on the ViaVoice engine, the basic algorithms employed, and their necessary modifications to ensure real-time performance and causality of the visual front end processing.  benchmark the resulting system performance on stored videos against prior research experiments, and  report a close match beten the two.
['Jonathan H. Connell', 'Norman Haas', 'Etienne Marcheret', 'Chalapathy Neti', 'Gerasimos Potamianos', 'Senem Velipasalar']
I am writing a research paper on a idea A real-time prototype for small-vocabulary audio-visual ASR
[{"from": "human", "value": "I am writing a research paper on a idea A real-time prototype for small-vocabulary audio-visual ASR"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a prototype for the automatic recognition of audio-visual speech, developed to augment the IBM ViaVoice/spl trade/ speech recognition system. Frontal face, full frame video is captured through a USB 2.0 interface by means of an inexpensive PC camera, and processed to obtain appearance-based visual features. Subsequently, these are combined with audio features, synchronously extracted from the acoustic signal, using a simple discriminant feature fusion technique. On the average, the required computations utilize approximately 67% of a Pentium/spl trade/ 4, 1.8 GHz processor, leaving the remaining resources available to hidden Markov model based speech recognition. Real-time performance is there- fore achieved for small-vocabulary tasks, such as connected-digit recognition. In the paper,  discuss the prototype architecture based on the ViaVoice engine, the basic algorithms employed, and their necessary modifications to ensure real-time performance and causality of the visual front end processing.  benchmark the resulting system performance on stored videos against prior research experiments, and  report a close match beten the two."}]