NDLI: Vowel based Voice Activity Detection with LSTM Recurrent Neural Network

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Vowel based Voice Activity Detection with LSTM Recurrent Neural Network

Content Provider	ACM Digital Library
Author	Kim, Juntae Lee, Seunghyung Hahn, Minsoo Park, Jinuk Kim, Jaeseok
Abstract	Voice activity detection (VAD) determines whether the incoming signal segments are speech or noiseand is an important technique in almost all of speech-related applications. In order to improve VAD performance in various noise environments, characterizing the speech feature has been the most crucial issue up to date. Among several proposed speech features, the context information of speech through time and vowel sound characteristics are known to current state-of-the-art speech features. Therefore, in order to reflect both on these merits, we propose vowel based VAD by Long short term memory recurrent neural network (LSTM-RNN). LSTM-RNN is known to the powerful model to capture dynamical context information through time. Moreover, with teaching the LSTM-RNN to only vowel sounds rather than whole speech, LSTM-RNN can learn more effectively because of the reduced manifold of speech. According to our experiments, proposed method shows better accuracy not only in the VAD task compared to LSTM-RNN based VAD but alsoa vowel detection task.
Starting Page	134
Ending Page	137
Page Count	4
File Format	PDF
ISBN	9781450347907
DOI	10.1145/3015166.3015207
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Voice activity detection Vowel sound Recurrent neural network
Content Type	Text
Resource Type	Article

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