Deep bidirectional long short-term memory recurrent neural networks for grapheme-to-phoneme conversion utilizing complex many-to-many alignments

Efficient grapheme-to-phoneme (G2P) conversion models are considered indispensable components to achieve the stateof- the-art performance in modern automatic speech recognition (ASR) and text-to-speech (TTS) systems. The role of these models is to provide such systems with a means to generate accurate pronunciations for unseen words. Recent work in this domain is based on recurrent neural networks (RNN) that are capable of translating grapheme sequences into phoneme sequences taking into account the full context of graphemes. To achieve high performance with these models, utilizing explicit alignment information is found essential. The quality of the G2P model heavily depends on the imposed alignment constraints. In this paper, a novel approach is proposed using complex many-to-many G2P alignments to improve the performance of G2P models based on deep bidirectional long short-term memory (BLSTM) RNNs. Extensive experiments cover models with different numbers of hidden layers, projection layer, input splicing windows, and varying alignment schemes. One observes that complex alignments significantly improve the performance on the publicly available CMUDict US English dataset. We compare our results with previously published results.