Research on Phoneme Recognition using Attention-based Methods

A phoneme is the smallest sound unit of a language. Every language has its corresponding phonemes. Phoneme recognition can be used in speech-based applications such as auto speech recognition and lip sync. This paper proposes an end-to-end deep learning model called Connectionist Temporal Classification (CTC) and attention-based seq2seq network that consists of one bi-GRU layer in the encoder and one GRU layer in the decoder, for recognizing the phonemes in speech. Experiments on the TIMIT dataset demonstrate its advantages on some other seq2seq networks, with over 50% improvements after applying the attention mechanism.