Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Long Short-Term Memory (LSTM) is a particular type of recurrent neural network (RNN) that can model long term temporal dynamics. Recently it has been shown that LSTM-RNNs can achieve higher recognition accuracy than deep feed-forword neural networks (DNNs) in acoustic modelling. Hover, speaker adaption for LSTM-RNN based acoustic models has not been ll investigated. ,  study the LSTM-RNN speaker-aware training that incorporates the speaker information during model training to normalise the speaker variability.  first present several speaker-aware training architectures, and then empirically evaluate three types of speaker representation: I-vectors, bottleneck speaker vectors and speaking rate. Furthermore, to factorize the variability in the acoustic signals caused by speakers and phonemes respectively,  investigate the speaker-aware and phone-aware joint training under the framework of multi-task learning. In AMI meeting speech transcription task, speaker-aware training of LSTM-RNNs reduces word error rates by 6.5% relative to a very strong LSTM-RNN baseline, which uses FMLLR features.
['Tian Tan', 'Yanmin Qian', 'Dong Yu', 'Souvik Kundu', 'Liang Lu', 'Khe Chai Sim', 'Xiong Xiao', 'Yu Zhang']
I am writing a research paper on a idea Speaker-aware training of LSTM-RNNS for acoustic modelling
[{"from": "human", "value": "I am writing a research paper on a idea Speaker-aware training of LSTM-RNNS for acoustic modelling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Long Short-Term Memory (LSTM) is a particular type of recurrent neural network (RNN) that can model long term temporal dynamics. Recently it has been shown that LSTM-RNNs can achieve higher recognition accuracy than deep feed-forword neural networks (DNNs) in acoustic modelling. Hover, speaker adaption for LSTM-RNN based acoustic models has not been ll investigated. ,  study the LSTM-RNN speaker-aware training that incorporates the speaker information during model training to normalise the speaker variability.  first present several speaker-aware training architectures, and then empirically evaluate three types of speaker representation: I-vectors, bottleneck speaker vectors and speaking rate. Furthermore, to factorize the variability in the acoustic signals caused by speakers and phonemes respectively,  investigate the speaker-aware and phone-aware joint training under the framework of multi-task learning. In AMI meeting speech transcription task, speaker-aware training of LSTM-RNNs reduces word error rates by 6.5% relative to a very strong LSTM-RNN baseline, which uses FMLLR features."}]