![]() ![]() Basically, we embed each character in the sentence and adopt the bi-directional long short-term memory (BLSTM) structure to accumulate the forward context information and backward context information as the conditional feature in the sentence-level. In the light of these two characteristics, we first design an encoder module using a recurrent neural network (RNN) structure to extract the sentence-level encoding feature as the context condition. Previous research works in polyphonic character show that: 1) The utilization of context is an effective way to solve the pronunciation disambiguation of Chinese polyphonic characters 2) Most polyphonic word, which comprises by polyphonic character, could be used to determine the pronunciation of the polyphonic character. Besides using the polyphonic character embedding feature as the network input, we obtain auxiliary features from the corresponding sentence as a condition for predicting the correct pronunciation. In this paper, we introduce a data-driven approach using the conditional neural network architecture for polyphone disambiguation. This issue is also considered to be a homograph problem, which has important applications in speech synthesis and is still not solved today. Therefore, other than the G2P system, the polyphone disambiguation system is developed to choose the correct pronunciation of a polyphonic character from several candidates based on the context. ![]() This kind of characters is called polyphonic characters. Yet one single Chinese character could have several different pronunciations in terms of different usages in a sentence. While the G2P system in English TTS synthesis system aims to produce the phoneme sequences for the out-of-lexicon words, The target of a G2P system in Chinese TTS synthesis system is to convert Chinese characters to pinyins (phoneme representations with Latin alphabet in Mandarin Chinese). It appears to be a suitable choice of using phonemes or syllables as units for a TTS synthesis system in a way for effective and better performance. However, the number considerably declines to 1300 when converting the characters into phonologically allowed syllables, and even less when using Latin alphabet representation. According to the characteristics of Mandarin Chinese, there are at least 13000 commonly used Chinese characters. G2P typically generates a sequence of phones from a sequence of characters or graphemes. The grapheme-to-phoneme (G2P) conversion is a fundamental front-end procedure in the Chinese Text-to-Speech (TTS) synthesis system, either the traditional HMM-based speech synthesis system or the End-to-End speech synthesis system. The experimental results show that both the sentence-level and the word-level conditional embedding features are able to attain good performance for Mandarin Chinese polyphone disambiguation. To further validate our choices on the conditional feature, we investigate polyphone disambiguation systems with multi-level conditions respectively. Our system achieves an accuracy of 94.69% on a publicly available polyphonic character dataset. One goal of polyphone disambiguation is to address the homograph problem existing in the front-end processing of Mandarin Chinese text-to-speech system. We obtain the word-level condition from a pre-trained word-to-vector lookup table. The system is composed of a bidirectional recurrent neural network component acting as a sentence encoder to accumulate the context correlations, followed by a prediction network that maps the polyphonic character embeddings along with the conditions to corresponding pronunciations. ![]() Saving Earth Britannica Presents Earth’s To-Do List for the 21st Century.This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation.Britannica Beyond We’ve created a new place where questions are at the center of learning.100 Women Britannica celebrates the centennial of the Nineteenth Amendment, highlighting suffragists and history-making politicians.COVID-19 Portal While this global health crisis continues to evolve, it can be useful to look to past pandemics to better understand how to respond today.Student Portal Britannica is the ultimate student resource for key school subjects like history, government, literature, and more.This Time in History In these videos, find out what happened this month (or any month!) in history.#WTFact Videos In #WTFact Britannica shares some of the most bizarre facts we can find.Demystified Videos In Demystified, Britannica has all the answers to your burning questions.Britannica Explains In these videos, Britannica explains a variety of topics and answers frequently asked questions.Britannica Classics Check out these retro videos from Encyclopedia Britannica’s archives. ![]()
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