A comparison of neural-based visual recognisers for speech activity detection
International Journal of Speech Technology(2022)
Abstract
Existing literature on speech activity detection (SAD) highlights different approaches within neural networks but does not provide a comprehensive comparison to these methods. This is important because such neural approaches often require hardware-intensive resources. In this article, we provide a comparative analysis of three different approaches: classification with still images (CNN model), classification based on previous images (CRNN model), and classification of sequences of images (Seq2Seq model). Our experimental results using the Vid-TIMIT dataset show that the CNN model can achieve an accuracy of 97% whereas the CRNN and Seq2Seq models increase the classification to 99%. Further experiments show that the CRNN model is almost as accurate as the Seq2Seq model (99.1% vs. 99.6% of classification accuracy, respectively) but 57% faster to train (326 vs. 761 secs. per epoch).
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Key words
Visual speech activity recognition,Convolutional neural networks,Recurrent neural networks
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