2015 GraphemetoPhonemeConversionUsin
- (Rao et al., 2015) ⇒ Kanishka Rao, Fuchun Peng, Haşim Sak, and Françoise Beaufays. (2015). “Grapheme-to-phoneme Conversion Using Long Short-term Memory Recurrent Neural Networks.” In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. doi:10.1109/ICASSP.2015.7178767
Subject Headings: Grapheme-to-Phoneme (G2P)
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Abstract
Grapheme-to-phoneme (G2P) models are key components in speech recognition and text-to-speech systems as they describe how words are pronounced. We propose a G2P model based on a Long Short-Term Memory (LSTM) recurrent neural network (RNN). In contrast to traditional joint-sequence based G2P approaches, LSTMs have the flexibility of taking into consideration the full context of graphemes and transform the problem from a series of grapheme-to-phoneme conversions to a word-to-pronunciation conversion. Training joint-sequence based G2P require explicit grapheme-to-phoneme alignments which are not straightforward since graphemes and phonemes don't correspond one-to-one. The LSTM based approach forgoes the need for such explicit alignments. We experiment with unidirectional LSTM (ULSTM) with different kinds of output delays and deep bidirectional LSTM (DBLSTM) with a connectionist temporal classification (CTC) layer. The DBLSTM-CTC model achieves a word error rate (WER) of 25.8% on the public CMU dataset for US English. Combining the DBLSTM-CTC model with a joint n-gram model results in a WER of 21.3%, which is a 9% relative improvement compared to the previous best WER of 23.4% from a hybrid system.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2015 GraphemetoPhonemeConversionUsin | Fuchun Peng Kanishka Rao Haşim Sak Françoise Beaufays | Grapheme-to-phoneme Conversion Using Long Short-term Memory Recurrent Neural Networks | 10.1109/ICASSP.2015.7178767 | 2015 |