印度语言多语种语音合成器的性能评价与比较

M. Jeeva, B. Ramani, P. Vijayalakshmi
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引用次数: 3

摘要

给定输入文本,文本到语音(TTS)系统期望产生人类听者高度可理解的语音信号。最先进的合成方法是:基于单元选择的连接语音合成(USS)和基于隐马尔可夫模型(HMM)的语音合成(HTS)。在USS方法中,根据给定的文本选择预录制的语音单元并将其连接以生成合成语音,而在HTS方法中,从语音单元中提取特征,并针对这些单元训练与上下文相关的hmm。这些模型被连接起来形成句子hmm,通过从给定文本中提取特征并将其传递给相应的源系统过滤器来合成语音。对于印度语言来说,为每种语言建立一个语音合成器是很费力的。在这项工作中,单语言和多语言语音合成器以最先进的方法开发,并对两种系统的性能进行了比较。基于印度语言之间的声学相似性,为泰米尔语、泰卢固语、马拉雅拉姆语和印地语这四种印度语言衍生出了一个通用的电话机和一个问题集。所开发的合成器的性能使用来自听众的平均意见评分(MOS)进行评估。单语和多语系统的平均MOS从2.57到3.88不等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance evaluation and comparison of multilingual speech synthesizers for Indian languages
Given an input text, a text-to-speech (TTS) system is expected to produce a speech signal that is highly intelligible to human listener. State-of-the art synthesis approaches are: unit selection-based concatenative speech synthesis (USS) and hidden Markov model (HMM)-based speech synthesis (HTS). In USS approach, pre-recorded speech units are selected according to the given text and concatenated to produce synthetic speech whereas in HTS approach, features are extracted from the speech units and the context dependent HMMs are trained for these units. These models are concatenated to form sentence HMMs, which synthesize speech for the given text, by extracting features from them and passing it through corresponding source-system filters. For Indian languages, building a speech synthesizer for each language is laborious. In this work, monolingual and multilingual speech synthesizers are developed in the state-of-the-art approaches and the performances are compared for both the systems. Based on the acoustic similarities across Indian languages, a common phoneset and a question set is derived for four of the Indian languages namely, Tamil, Telugu, Malayalam, and Hindi. The performance of the synthesizers developed are evaluated using mean opinion score (MOS) derived from the listeners. The average MOS ranges from 2.57 to 3.88 for the monolingual and multilingual systems.
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