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Speech Synthesis Based on Hidden Markov Models

机译:基于隐马尔可夫模型的语音合成

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摘要

This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.
机译:本文给出了基于隐马尔可夫模型(HMM)的语音合成的总体概述,最近已证明该方法在合成语音方面非常有效。这种方法的主要优点是它可以灵活地改变说话者的身份,情绪和说话方式。本文还讨论了基于HMM的方法与最近几十年来占主导地位的更常规的单元选择方法之间的关系。最后,描述了用于未来发展的先进技术。

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