Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  examines the performance of a Distributed Speech Recognition (DSR) system in the presence of both background noise and packet loss. Recognition performance is examined for feature vectors extracted from speech using a physiologically-based auditory model, as an alternative to the more commonly-used Mel Frequency Cepstral Coefficient (MFCC) front-end. The feature vectors produced by the auditory model are vector quantised and combined in pairs for transmission over a statistically modelled channel that is subject to packet burst loss. In order to improve recognition performance in the presence of noise, the speech is enhanced prior to feature extraction using Wiener filtering. Packet loss mitigation to compensate for missing features is also used to further improve performance. Speech recognition results show the benefit of combining speech enhancement and packet loss mitigation to compensate for channel and environmental degradations.
['Ronan Flynn', 'Edward Jones']
I am writing a research paper on a idea Robust distributed speech recognition in noise and packet loss conditions
[{"from": "human", "value": "I am writing a research paper on a idea Robust distributed speech recognition in noise and packet loss conditions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  examines the performance of a Distributed Speech Recognition (DSR) system in the presence of both background noise and packet loss. Recognition performance is examined for feature vectors extracted from speech using a physiologically-based auditory model, as an alternative to the more commonly-used Mel Frequency Cepstral Coefficient (MFCC) front-end. The feature vectors produced by the auditory model are vector quantised and combined in pairs for transmission over a statistically modelled channel that is subject to packet burst loss. In order to improve recognition performance in the presence of noise, the speech is enhanced prior to feature extraction using Wiener filtering. Packet loss mitigation to compensate for missing features is also used to further improve performance. Speech recognition results show the benefit of combining speech enhancement and packet loss mitigation to compensate for channel and environmental degradations."}]