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End-to-End Deep Neural Network for Automatic Speech Recognition

We investigate the efficacy of deep neural networks on speech recognition. Specifically, we implement an end-to-end deep learning system that utilizes mel-filter bank features to directly output to spoken phonemes without the need of a traditional Hidden Markov Model for decoding. The system will comprise of two variants of neural networks for phoneme recognition. In particular, we utilize convolutional for frame level classification and recurrent architecture with Connectionist Temporal Classification loss for decoding the frames into a sequence of phonemes. We carry out our experiments on the TIMIT dataset. We have managed to obtain 22.1% for the frame error rate with our CNN, which to our knowledge, closely matches the state-of-the-art. Our decoded phone sequence achieves an error of 29.4%.