Grapheme-to-Phoneme Conversion with Convolutional Neural Networks. Yolchuyeva, S., Németh, G., & Gyires-Tóth, B. Applied Sciences, 9(6):1143, January, 2019. Number: 6 Publisher: Multidisciplinary Digital Publishing Institute![Grapheme-to-Phoneme Conversion with Convolutional Neural Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate.
@article{yolchuyeva_grapheme--phoneme_2019,
title = {Grapheme-to-{Phoneme} {Conversion} with {Convolutional} {Neural} {Networks}},
volume = {9},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {2076-3417},
url = {https://www.mdpi.com/2076-3417/9/6/1143},
doi = {10.3390/app9061143},
abstract = {Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate.},
language = {en},
number = {6},
urldate = {2022-10-08},
journal = {Applied Sciences},
author = {Yolchuyeva, Sevinj and Németh, Géza and Gyires-Tóth, Bálint},
month = jan,
year = {2019},
note = {Number: 6
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {1D convolution, Bi-LSTM, LSTM, encoder-decoder, grapheme-to-phoneme (G2P), residual architecture},
pages = {1143},
}
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