基于整句相似性计算的韵律短语预测模型

语音合成系统中，韵律短语的预测对合成语音的自然度有重要影响．为了突破主流的基于决策树预测方法的若干缺陷，提出了基于整句相似性计算的韵律短语预测模型．通过对1000个句子的测试，该方法在可接受的语料手工标注工作量的范围内，超过了传统决策树的方法．     

面向汉语统计参数语音合成的标注生成方法

针对汉语统计参数语音合成中的上下文相关标注生成,设计了声韵母层、音节层、词层、韵律词层、韵律短语层和语句层6层上下文相关的标注格式。对输入的中文语句进行文本规范并利用语法分析获得语句的结构和分词信息;通过字音转换获得每个汉字的声韵母及声调;利用TBL（Transformation-Based error driven Learning）算法预测输入文本的韵律词边界和韵律短语边界。在此基础上,获得输入文本中每个汉字的声韵母信息及其上下文结构信息,从而产生统计参数语音合成所需的上下文相关标注。设计了一个以声韵母为合成基元的普通话的基于隐Markov模型（HMM）的统计参数语音合成系统,通过主、客观实验评测了不同标注信息对合成语音音质的影响,结果表明,上下文相关的标注信息越丰富,合成语音的音质越好。     

基于韵律特征和语法信息的韵律边界检测模型

韵律短语边界的自动检测,对语音合成中语料库的韵律标注以及语音识别中韵律短语的自动划分都有重要意义。本文通过对影响韵律短语边界的声学、韵律等参量的分析,得到和韵律短语边界关联性较大的一组声学特征参数、韵律环境参数和语法信息;同时引入语音合成中的韵律预测思想,在假定所有音节边界均为非韵律短语边界时,预测每个音节的基频。最后使用决策树模型,将音节边界处的韵律环境信息、语法信息以及预测结果作为决策树的输入,利用决策树综合判定当前音节边界是否为韵律短语的边界。实验表明,这种方法对于基于确定性文本(text-dependent)的语音韵律短语边界的检测,具有较好效果,同时可以显著提高语音合成中语料库的标注效率和标注结果的一致性。     

基于声学相关特征与词典语法相关特征的汉语重音检测

重音对提高语音合成系统的自然度、可懂度以及语音识别系统的正确率等方面扮演着非常重要的作用.该文基于大规模韵律标注的语料库,利用声学相关特征及词典语法相关特征对汉语重音进行检测.采用Boosting 集成分类回归树对当前音节的声学相关特征以及词典语法相关特征进行建模,Boosting集成分类回归树充分利用了当前音节的特性...     

基于韵律特征参数的情感语音合成算法研究

为了合成更为自然的情感语音,提出了基于语音信号声学韵律参数及时域基音同步叠加算法的情感语音合成系统.实验通过对情感语音数据库中生气、无聊、高兴和悲伤4种情感的韵律参数分析,建立4种情感模板,采用波形拼接语音合成技术,运用时域基音同步叠加算法合成含有目标感情色彩的语音信号.实验结果表明,运用波形拼接算法,调节自然状态下语音信号的韵律特征参数,可合成较理想的情感语音.合成的目标情感语音具有明显的感情色彩,其主观情感类别判别正确率较高.     

基于广义回归神经网络的情感语音韵律特征预测

韵律特征是语音信号中情感信息的主要表征之一。为了更好地进行情感语音合成的研究,本文通过提取普通话情感语音的韵律特征进行分析,采用广义回归神经网络构建了一个情感语音韵律特征预测模型,并根据所提取的测试集数据文本语境信息进行韵律特征预测,实验获得了相应的结果。实验结果表明,情感语音韵律特征预测效果较好。     

语音合成系统中高质量的韵律生成

本文对富士通中文语音合成系统尤其是其中的韵律生成部分进行了描述。该系统是一个以音节为基本合成单元,在韵律参数生成结果即音长和基频预测结果的指导下,从音库中搜寻全局最优的合成单元,然后采用PSOLA算法进行波形调整的拼接合成系统。从提高合成语音韵律的角度出发,本文围绕音长预测和基频预测部分对该系统进行了详细的描述。最后,给出了韵律评测和系统评测的结果。     

基于汉语节律特征描述的语音合成模型

汉语节律的合理使用能使合成语音表现出语篇的正确内涵和感情色彩。本文介绍了一种基于汉语节律特征描述的语音合成模型。本文首先介绍了汉语节律的停延、词重音、句重音、变调、调模等节律特征的分析和提取,详细描述了节律特征的各类情形,并阐述了基于汉语节律的语音合成算法模型,包括切词、标注、分析、定模、修正、输出的处理流程和合成语音声学参数序列{（h,l,s）}的生成。最后,给出了语音合成模型的实验结果与分析。     

基于关联规则的藏语语音韵律参数提取

韵律规则对于语音识别和语音合成具有重要意义,韵律特征参数的描述正确与否直接影响合成系统的输出.为了提高藏语语音合成中语音的自然度,本文研究了基于数据挖掘中的关联规则来发现韵律参数之间的相互关系,并基于关联规则算法获得藏语韵律参数中基频参数的变化规则,这些规则可以为藏语语音合成系统的选音提供帮助.     

中文语音合成系统的设计与实现

为了实现机器能够发出声音,本文设计并搭建了HTK（HMM-Tool-Kit）平台用来实现中文语音合成系统.采用参数合成法实现了文本到语音的合成,并对合成系统中的文本分析、韵律控制以及语音合成的实现技术进行了详细的论述.最后在Linux系统下搭建环境并进行实验,得到了预期的结果,实现了文本到语音的转化.     

Prosody dependent speech recognition on radio news corpus of American English

Does prosody help word recognition? This paper proposes a novel probabilistic framework in which word and phoneme are dependent on prosody in a way that reduces word error rates (WER) relative to a prosody-independent recognizer with comparable parameter count. In the proposed prosody-dependent speech recognizer, word and phoneme models are conditioned on two important prosodic variables: the intonational phrase boundary and the pitch accent. An information-theoretic analysis is provided to show that prosody dependent acoustic and language modeling can increase the mutual information between the true word hypothesis and the acoustic observation by exciting the interaction between prosody dependent acoustic model and prosody dependent language model. Empirically, results indicate that the influence of these prosodic variables on allophonic models are mainly restricted to a small subset of distributions: the duration PDFs (modeled using an explicit duration hidden Markov model or EDHMM) and the acoustic-prosodic observation PDFs (normalized pitch frequency). Influence of prosody on cepstral features is limited to a subset of phonemes: for example, vowels may be influenced by both accent and phrase position, but phrase-initial and phrase-final consonants are independent of accent. Leveraging these results, effective prosody dependent allophonic models are built with minimal increase in parameter count. These prosody dependent speech recognizers are able to reduce word error rates by up to 11% relative to prosody independent recognizers with comparable parameter count, in experiments based on the prosodically-transcribed Boston Radio News corpus.     

An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS

Building a text corpus suitable to be used in corpus-based speech synthesis is a time-consuming process that usually requires some human intervention to select the desired phonetic content and the necessary variety of prosodic contexts. If an emotional text-to-speech (TTS) system is desired, the complexity of the corpus generation process increases. This paper presents a study aiming to validate or reject the use of a semantically neutral text corpus for the recording of both neutral and emotional (acted) speech. The use of this kind of texts would eliminate the need to include semantically emotional texts into the corpus. The study has been performed for Basque language. It has been made by performing subjective and objective comparisons between the prosodic characteristics of recorded emotional speech using both semantically neutral and emotional texts. At the same time, the performed experiments allow for an evaluation of the capability of prosody to carry emotional information in Basque language. Prosody manipulation is the most common processing tool used in concatenative TTS. Experiments of automatic recognition of the emotions considered in this paper (the "Big Six emotions") show that prosody is an important emotional indicator, but cannot be the only manipulated parameter in an emotional TTS system-at least not for all the emotions. Resynthesis experiments transferring prosody from emotional to neutral speech have also been performed. They corroborate the results and support the use of a neutral-semantic-content text in databases for emotional speech synthesis.     

基于语块的英语文语转换系统的韵律生成方法

为预测英语文语转换(Text-to-Speech,TIS)系统中韵律生成模块的韵律边界,通过在中间短语、语调短语和语句后分别插入不同长度的停顿,产生使合成语音具有与真人语音类似的韵律结构.通过采用基于语块的中间短语切分,以中间短语为基本单位,生成一个语调短语边界预测的学习语料库,然后采用转换式学习法进行标注学习,从而实现韵律边界的切分.在对真人语料库进行测试的实验中,标注正确率达到81.32%,通过在学习中增加语调短语音节数和标点符号的约束规则,可进一步提高标注正确率.     

汉语朗读话语重音自动分类研究

汉语的重音由于受到声调、语调以及韵律单元层级的干扰和制约,对于重音的自动感知一直是比较困难的问题。针对标准的朗读普通话语,本文在广义韵律结构的框架下研究了重音的声学表现,设计并实现了重音的自动感知模型。本文提出的基于分类树结构的区分度模型能有效地结合韵律单元结构对重音的制约。研究结果表明,音高高线、调域、音长是表达重音最重要线索,利用这些线索能有效地实现对重音的自动感知。我们的模型能一般能达到80 %左右的重音检出水平。     

Syllable based text to speech synthesis system using auto associative neural network prosody prediction

This paper presents the design and development of an Auto Associative Neural Network (AANN) based unrestricted prosodic information synthesizer. Unrestricted Text To Speech System (TTS) is capable of synthesize different domain speech with improved quality. This paper deals with a corpus-driven text-to speech system based on the concatenative synthesis approach. Concatenative speech synthesis involves the concatenation of the basic units to synthesize an intelligent, natural sounding speech. A corpus-based method (unit selection) uses a large inventory to select the units and concatenate. The prosody prediction is done with the help of five layer auto associative neural network which helps us to improve the quality of speech synthesis. Here syllables are used as basic unit of speech synthesis database. The database consisting of the units along with their annotated information is called annotated speech corpus. A clustering technique is used in annotated speech corpus that provides way to select the appropriate unit for concatenation, based on the lowest total join cost of the speech unit. Discontinuities present at the unit boundaries are lowered by using the mel-LPC smoothing technique. The experiment has been made for the Dravidian language Tamil and the results reveal to demonstrate the improved intelligibility and naturalness of the proposed method. The proposed system is applicable to all the languages if the syllabification rules has been changed.     

汉语语音合成语料库管理系统的建立

本文介绍的语料库管理系统主要用于语音合成的研究或开发工作 .语料的设计考虑了音段和韵律 ,语料库中包括汉语的音节、词语、独白语句和情景对话语篇 ,语音的录制是在卦限录音室完成 .管理系统对各种语音数据进行综合有效的管理 ,它具有查询、浏览和更新等功能     

Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework

In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic-prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic-syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87% on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling.     

The Prosody Structure of Dialogue Components in Hungarian

Prosody is the change of F0 and intensity in time and the speed of articulation. The presence or absence of the realization of word accent is also examined as an important feature in prosody generation. During verbal communication various prosody forms contribute to the expression of the textual content of the message on the one hand and of the personal intention of the speaker on the other. In many cases in dialogues the same text can be (must be) pronounced with different intentions. Our goal was to find what kind of prosody patterns and rules are characteristic of these utterance types and what the acoustic relationship among them is for Hungarian. In this article the prosody structures of the most important dialogue components are described, and invariant structures are derived and verified by speech synthesis. Rules are also stated as generalized function structures to show the acoustic relationship of the prosody of these expressions to the prosody of statements. Using these rules, it is possible to convert the prosody of a given utterance type to another one by preserving the naturalness of the speech. The rules can be used in text to speech (TTS) conversion to generate spoken dialogues.     

Unsupervised Adaptation of Categorical Prosody Models for Prosody Labeling and Speech Recognition

Automatic speech recognition (ASR) systems rely almost exclusively on short-term segment-level features (MFCCs), while ignoring higher level suprasegmental cues that are characteristic of human speech. However, recent experiments have shown that categorical representations of prosody, such as those based on the Tones and Break Indices (ToBI) annotation standard, can be used to enhance speech recognizers. However, categorical prosody models are severely limited in scope and coverage due to the lack of large corpora annotated with the relevant prosodic symbols (such as pitch accent, word prominence, and boundary tone labels). In this paper, we first present an architecture for augmenting a standard ASR with symbolic prosody. We then discuss two novel, unsupervised adaptation techniques for improving, respectively, the quality of the linguistic and acoustic components of our categorical prosody models. Finally, we implement the augmented ASR by enriching ASR lattices with the adapted categorical prosody models. Our experiments show that the proposed unsupervised adaptation techniques significantly improve the quality of the prosody models; the adapted prosodic language and acoustic models reduce binary pitch accent (presence versus absence) classification error rate by 13.8% and 4.3%, respectively (relative to the seed models) on the Boston University Radio News Corpus, while the prosody-enriched ASR exhibits a 3.1% relative reduction in word error rate (WER) over the baseline system.