Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  focuses on the recognition of noisy speech.  show that the decoding of a noisy speech waveform can be facilitated if the recognizer has explicit knowledge of where it should hypothesize speech phones, and where it should map the acoustics to non-speech phones.  build a speech/non-speech detector and use its output as an additional front-end feature.  show that by appropriately ighting the contribution of this feature in the decoder and by modifying the acoustic models accordingly,  can penalize speech/non-speech confusions and consequently reduce the recognition error rate. This approach gives a 12% overall error rate reduction on a wide variety of recognition tasks and noise characteristics without degrading performance on clean test data. A simple extension of the approach boosts recognition improvements on noisy test sets to 14% overall.
['Françoise Beaufays', 'Daniel Boies', 'Mitch Weintraub', 'Qifeng Zhu 0001']
I am writing a research paper on a idea Using speech/non-speech detection to bias recognition search on noisy data
[{"from": "human", "value": "I am writing a research paper on a idea Using speech/non-speech detection to bias recognition search on noisy data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  focuses on the recognition of noisy speech.  show that the decoding of a noisy speech waveform can be facilitated if the recognizer has explicit knowledge of where it should hypothesize speech phones, and where it should map the acoustics to non-speech phones.  build a speech/non-speech detector and use its output as an additional front-end feature.  show that by appropriately ighting the contribution of this feature in the decoder and by modifying the acoustic models accordingly,  can penalize speech/non-speech confusions and consequently reduce the recognition error rate. This approach gives a 12% overall error rate reduction on a wide variety of recognition tasks and noise characteristics without degrading performance on clean test data. A simple extension of the approach boosts recognition improvements on noisy test sets to 14% overall."}]