Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In speech recognition, it is preferable not to hypothesize the details, e.g., specific age and gender, of a target user. Hover, speaker independence is one of the things that degrades ASR performance. In this work,  propose a speaker adaptation method to recognize a short time utterance. There have been several studies on speaker-independent DNN-HMM in which i-vector is computed, and the additional information is combined with acoustic features. Hover, it is difficult to calculate i-vector accurately or apply speaker adaptation (e.g. fMLLR) when the utterance time is short (0.5sec∼). In our approach,  calculate the similarity score beten the speaker class and the target utterance and utilize speaker class information configured in advance. As a precondition,  restrict the available time period to the first 50 frames per utterance for the recognition of short utterances. In experimental tests,  obtained a 4.0% relative WER gain compared to conventional DNN-HMM.
['Hiroshi Seki', 'Kazumasa Yamamoto', 'Seiichi Nakagawa']
I am writing a research paper on a idea Deep neural network based acoustic model using speaker-class information for short time utterance
[{"from": "human", "value": "I am writing a research paper on a idea Deep neural network based acoustic model using speaker-class information for short time utterance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In speech recognition, it is preferable not to hypothesize the details, e.g., specific age and gender, of a target user. Hover, speaker independence is one of the things that degrades ASR performance. In this work,  propose a speaker adaptation method to recognize a short time utterance. There have been several studies on speaker-independent DNN-HMM in which i-vector is computed, and the additional information is combined with acoustic features. Hover, it is difficult to calculate i-vector accurately or apply speaker adaptation (e.g. fMLLR) when the utterance time is short (0.5sec\u223c). In our approach,  calculate the similarity score beten the speaker class and the target utterance and utilize speaker class information configured in advance. As a precondition,  restrict the available time period to the first 50 frames per utterance for the recognition of short utterances. In experimental tests,  obtained a 4.0% relative WER gain compared to conventional DNN-HMM."}]