TEMPORAL MODELING USING DILATED CONVOLUTION AND GATING FOR VOICE-ACTIVITY-DETECTION
Abstract
Voice-activity-detection (VAD) is the task of predicting where in
the utterance is speech versus background noise. It is an important
first step to determine when to open the microphone (i.e., start-of-
speech) and close the microphone (i.e., end-of-speech) for streaming
speech recognition applications such as Voice Search. Long short-
term memory neural networks (LSTMs) have been a popular archi-
tecture for sequential modeling for acoustic signals, and have been
successfully used for many VAD applications. However, it has been
observed that LSTMs suffer from state saturation problems when the
utterance is long (i.e., for voice dictation tasks), and thus requires the
LSTM state to be periodically reset. In this paper, we propse an alter-
native architecture that does not suffer from saturation problems by
modeling temporal variations through a stateless dilated convolution
neural network (CNN). The proposed architecture differs from con-
ventional CNNs in three respects (1) dilated causal convolution, (2)
gated activations and (3) residual connections. Results on a Google Voice
Typing task shows that the proposed architecture achieves 14% rela-
tive FA improvement at a FR of 1% over state-of-the-art LSTMs for
VAD task. We also include detailed experiments investigating the
factors that distinguish the proposed architecture from conventional
convolution.
the utterance is speech versus background noise. It is an important
first step to determine when to open the microphone (i.e., start-of-
speech) and close the microphone (i.e., end-of-speech) for streaming
speech recognition applications such as Voice Search. Long short-
term memory neural networks (LSTMs) have been a popular archi-
tecture for sequential modeling for acoustic signals, and have been
successfully used for many VAD applications. However, it has been
observed that LSTMs suffer from state saturation problems when the
utterance is long (i.e., for voice dictation tasks), and thus requires the
LSTM state to be periodically reset. In this paper, we propse an alter-
native architecture that does not suffer from saturation problems by
modeling temporal variations through a stateless dilated convolution
neural network (CNN). The proposed architecture differs from con-
ventional CNNs in three respects (1) dilated causal convolution, (2)
gated activations and (3) residual connections. Results on a Google Voice
Typing task shows that the proposed architecture achieves 14% rela-
tive FA improvement at a FR of 1% over state-of-the-art LSTMs for
VAD task. We also include detailed experiments investigating the
factors that distinguish the proposed architecture from conventional
convolution.
Research Areas
