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        <title>Compress Polyphone Pronunciation Prediction Model with Shared Labels</title>
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        <namePart type="given">Pengfei</namePart>
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        <namePart type="given">Hui</namePart>
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        <namePart type="given">Kazushige</namePart>
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    <name type="personal">
        <namePart type="given">Lvhong</namePart>
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            <title>Proceedings of the 19th Chinese National Conference on Computational Linguistics</title>
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            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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            <namePart type="given">Sujian</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>It is well known that deep learning model has huge parameters and is computationally expensive, especially for embedded and mobile devices. Polyphone pronunciations selection is a basic function for Chinese Text-to-Speech (TTS) application. Recurrent neural network (RNN) is a good sequence labeling solution for polyphone pronunciation selection. However, huge parameters and computation make compression needed to alleviate its disadvantage. In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection. The basic idea is to compress large number of target labels into a few label clusters, which will share the parameters of fully-connected layer. Furthermore, we combine it with other methods to further compress the polyphone pronunciation selection model. The experimental result shows that for Bi-LSTM (Bidirectional Long Short Term Memory) based polyphone selection, shared labels model decreases about 52% of original model size and accelerates prediction by 44% almost without performance loss. It is worth mentioning that the proposed method can be applied for other tasks to compress the model and accelerate the calculation.</abstract>
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%0 Conference Proceedings
%T Compress Polyphone Pronunciation Prediction Model with Shared Labels
%A Chen, Pengfei
%A Wang, Lina
%A Di, Hui
%A Ouchi, Kazushige
%A Wang, Lvhong
%Y Sun, Maosong
%Y Li, Sujian
%Y Zhang, Yue
%Y Liu, Yang
%S Proceedings of the 19th Chinese National Conference on Computational Linguistics
%D 2020
%8 October
%I Chinese Information Processing Society of China
%C Haikou, China
%G English
%F chen-etal-2020-compress
%X It is well known that deep learning model has huge parameters and is computationally expensive, especially for embedded and mobile devices. Polyphone pronunciations selection is a basic function for Chinese Text-to-Speech (TTS) application. Recurrent neural network (RNN) is a good sequence labeling solution for polyphone pronunciation selection. However, huge parameters and computation make compression needed to alleviate its disadvantage. In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection. The basic idea is to compress large number of target labels into a few label clusters, which will share the parameters of fully-connected layer. Furthermore, we combine it with other methods to further compress the polyphone pronunciation selection model. The experimental result shows that for Bi-LSTM (Bidirectional Long Short Term Memory) based polyphone selection, shared labels model decreases about 52% of original model size and accelerates prediction by 44% almost without performance loss. It is worth mentioning that the proposed method can be applied for other tasks to compress the model and accelerate the calculation.
%U https://aclanthology.org/2020.ccl-1.104
%P 1122-1131

Markdown (Informal)

[Compress Polyphone Pronunciation Prediction Model with Shared Labels](https://aclanthology.org/2020.ccl-1.104) (Chen et al., CCL 2020)

• Compress Polyphone Pronunciation Prediction Model with Shared Labels (Chen et al., CCL 2020)

ACL

• Pengfei Chen, Lina Wang, Hui Di, Kazushige Ouchi, and Lvhong Wang. 2020. Compress Polyphone Pronunciation Prediction Model with Shared Labels. In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 1122–1131, Haikou, China. Chinese Information Processing Society of China.