Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-Level Embedding Features

Cai, Zexin; Yang, Yaogen; Zhang, Chuxiong; Qin, Xiaoyi; Li, Ming

doi:10.21437/interspeech.2019-1235

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…Text Normalization Rule-based [311], Neural-based [310,223,406,430], Hybrid [432] Word Segmentation [394,444,261] POS Tagging [292,323,221,444,135] Prosody Prediction [50,405,312,186,137,322,277,62,440,210,212,3] Grapheme to Phoneme N-gram [41,24], Neural-based [403,283,33, 320] --Polyphone Disambiguation [441,392,224,295,321,29,257] and then neural networks are leveraged to model text normalization as a sequence to sequence task where the source and target sequences are non-standard words and spoken-form words respectively [310,223,430]. Recently, some works [432] propose to combine the advantages of both rule-based and neural-based models to further improve the performance of text normalization.…”

Section: Task Research Workmentioning

confidence: 99%

A Survey on Neural Speech Synthesis

Tan,

Qin,

Soong

et al. 2021

Preprint

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Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural speech given text, is a hot research topic in speech, language, and machine learning communities and has broad applications in the industry. As the development of deep learning and artificial intelligence, neural network-based TTS has significantly improved the quality of synthesized speech in recent years. In this paper, we conduct a comprehensive survey on neural TTS, aiming to provide a good understanding of current research and future trends. We focus on the key components in neural TTS, including text analysis, acoustic models, and vocoders, and several advanced topics, including fast TTS, low-resource TTS, robust TTS, expressive TTS, and adaptive TTS, etc. We further summarize resources related to TTS (e.g., datasets, opensource implementations) and discuss future research directions. This survey can serve both academic researchers and industry practitioners working on TTS.

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Section: Task Research Workmentioning

confidence: 99%

A Survey on Neural Speech Synthesis

Tan,

Qin,

Soong

et al. 2021

Preprint

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“…Some studies have explored solving PD by regarding pronunciation estimation (including non-polyphonic words) as a sequenceto-sequence problem, and by applying machine translation approaches [13,14]. For Mandarin, on the other hand, some studies adopt a classification approach that estimates the correct pinyin of the polyphonic character [15,16,17]. Because polyphonic words appear only in certain parts of the sentence, we regard PD as a classification problem, similar to the approach for Mandarin.…”

Section: Polyphone Disambiguation (Pd)mentioning

confidence: 99%

Polyphone disambiguation and accent prediction using pre-trained language models in Japanese TTS front-end

Hida¹,

Hamada²,

Kamada³

et al. 2022

Preprint

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Although end-to-end text-to-speech (TTS) models can generate natural speech, challenges still remain when it comes to estimating sentence-level phonetic and prosodic information from raw text in Japanese TTS systems. In this paper, we propose a method for polyphone disambiguation (PD) and accent prediction (AP). The proposed method incorporates explicit features extracted from morphological analysis and implicit features extracted from pre-trained language models (PLMs). We use BERT and Flair embeddings as implicit features and examine how to combine them with explicit features. Our objective evaluation results showed that the proposed method improved the accuracy by 5.7 points in PD and 6.0 points in AP. Moreover, the perceptual listening test results confirmed that a TTS system employing our proposed model as a front-end achieved a mean opinion score close to that of synthesized speech with groundtruth pronunciation and accent in terms of naturalness.

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“…Homograph. Some characters could be pronounced in multiple ways depending on the textual context they resides [20]. 2.…”

Section: The Aishell-3 Datasetmentioning

confidence: 99%

AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines

Shi,

Bu,

et al. 2020

Preprint

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In this paper, we present AISHELL-3, a large-scale and high-fidelity multi-speaker Mandarin speech corpus which could be used to train multi-speaker Text-to-Speech (TTS) systems. The corpus contains roughly 85 hours of emotion-neutral recordings spoken by 218 native Chinese mandarin speakers. Their auxiliary attributes such as gender, age group and native accents are explicitly marked and provided in the corpus. Accordingly, transcripts in Chinese characterlevel and pinyin-level are provided along with the recordings. We present a baseline system that uses AISHELL-3 for multi-speaker Madarin speech synthesis. The multi-speaker speech synthesis system is an extension on Tacotron-2 where a speaker verification model and a corresponding loss regarding voice similarity are incorporated as the feedback constraint. We aim to use the presented corpus to build a robust synthesis model that is able to achieve zero-shot voice cloning. The system trained on this dataset also generalizes well on speakers that are never seen in the training process. Objective evaluation results from our experiments show that the proposed multispeaker synthesis system achieves high voice similarity concerning both speaker embedding similarity and equal error rate measurement. The dataset 1 , baseline system code and generated samples 2 are available online.

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Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-Level Embedding Features

Cited by 17 publications

References 17 publications

A Survey on Neural Speech Synthesis

A Survey on Neural Speech Synthesis

Polyphone disambiguation and accent prediction using pre-trained language models in Japanese TTS front-end

AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines

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