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Junyoung Chung

Alumni

Publications

Dynamic Frame Skipping for Fast Speech Recognition in Recurrent Neural Network Based Acoustic Models
Inchul Song
Junyoung Chung
Taesup Kim
Yoshua Bengio
A recurrent neural network is a powerful tool for modeling sequential data such as text and speech. While recurrent neural networks have ach… (voir plus)ieved record-breaking results in speech recognition, one remaining challenge is their slow processing speed. The main cause comes from the nature of recurrent neural networks that read only one frame at each time step. Therefore, reducing the number of reads is an effective approach to reducing processing time. In this paper, we propose a novel recurrent neural network architecture called Skip-RNN, which dynamically skips speech frames that are less important. The Skip-RNN consists of an acoustic model network and skip-policy network that are jointly trained to classify speech frames and determine how many frames to skip. We evaluate our proposed approach on the Wall Street Journal corpus and show that it can accelerate acoustic model computation by up to 2.4 times without any noticeable degradation in transcription accuracy.
2018-04-15
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org