Context Adaptive Neural Network for Rapid Adaptation of Deep CNN Based Acoustic Models

@inproceedings{Delcroix2016ContextAN,
  title={Context Adaptive Neural Network for Rapid Adaptation of Deep CNN Based Acoustic Models},
  author={Marc Delcroix and Keisuke Kinoshita and Atsunori Ogawa and Takuya Yoshioka and Dung T. Tran and Tomohiro Nakatani},
  booktitle={INTERSPEECH},
  year={2016}
}
Using auxiliary input features has been seen as one of the most effective ways to adapt deep neural network (DNN)-based acoustic models to speaker or environment. However, this approach has several limitations. It only performs compensation of the bias term of the hidden layer and therefore does not fully exploit the network capabilities. Moreover, it may not be well suited for certain types of architectures such as convolutional neural networks (CNNs) because the auxiliary features have… CONTINUE READING