ISCA Archive Interspeech 2012
ISCA Archive Interspeech 2012

A initial attempt on task-specific adaptation for deep neural network-based large vocabulary continuous speech recognition

Yeming Xiao, Zhen Zhang, Shang Cai, Jielin Pan, Yonghong Yan

In the state-of-the-art automatic speech recognition (ASR) systems, adaption techniques are used to the mitigate performance degradation caused by the mismatch in the training and testing procedure. Although there are bunch of adaption techniques for the hidden Markov models (HMM)-GMM-based system, there is rare work about the adaption in the hybrid artificial neural network~(ANN)/HMM-based system. Recently, there is a resurgence on ANN/HMM scheme for ASR with the success of context dependent deep neural network HMM~(CD-DNN/ HMM). Therefore in this paper, we present our initial efforts on the adaption techniques in the CD-DNN/HMM system. Specially, a linear input network(LIN)-based method and a neural network retraining(NNR)-based method is experimentally explored for the the task-adaptation purpose. Experiments on conversation telephone speech data set shows that these techniques can improve the system significantly and LINbased method seems to work better with medium mount of adaptation data.

Index Terms: deep neural network, pre-training, speaker adaptation, LVCSR


doi: 10.21437/Interspeech.2012-9

Cite as: Xiao, Y., Zhang, Z., Cai, S., Pan, J., Yan, Y. (2012) A initial attempt on task-specific adaptation for deep neural network-based large vocabulary continuous speech recognition. Proc. Interspeech 2012, 2574-2577, doi: 10.21437/Interspeech.2012-9

@inproceedings{xiao12_interspeech,
  author={Yeming Xiao and Zhen Zhang and Shang Cai and Jielin Pan and Yonghong Yan},
  title={{A initial attempt on task-specific adaptation for deep neural network-based large vocabulary continuous speech recognition}},
  year=2012,
  booktitle={Proc. Interspeech 2012},
  pages={2574--2577},
  doi={10.21437/Interspeech.2012-9},
  issn={2958-1796}
}