语音合成技术与挑战

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摘要翻译：
本项目的目的是开发和实现一个英语文本-语音合成系统。这包括对人类语音产生机制的研究，对语音合成技术的回顾，以及对用于评估合成语音有效性的测试的分析。确定diphone合成系统是本项目范围内最有效的选择。设计了一种从提示语音中自动识别和提取diphone的方法，允许说话人在不到40分钟的时间内创建diphone数据库。用CMUdict确定已知词的发音。设计并实现了一个用于平滑双电话录音间转换的系统。然后用CMUdict训练一个最大似然预测系统来确定未知英语字母词的正确发音。然后，设计了一个词类标注器来查找句子中的词类。将TD-PSOLA算法和一种新的清音时长移位方法相结合，设计了一种随时间改变产生的语音的基音、时长和音量的方法。这在移动音高或持续时间时最小化了语音失真，同时通过在时域中操作最大化了计算效率。这种方法被用来为单词中的元音添加正确的词汇重音。开发了一个文本标记系统来处理任意文本输入，允许数字输入标记的发音和使用适当的停顿进行标点符号。讨论了进一步提高句子层次语音自然度的方法。最后，对系统的可理解性和自然性进行了测试。
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英文标题：
《Techniques and Challenges in Speech Synthesis》
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作者：
David Ferris
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Sound        声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
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英文摘要：
  The aim of this project was to develop and implement an English language Text-to-Speech synthesis system. This involved a study of mechanisms of human speech production, a review of techniques in speech synthesis, and analysis of tests used to evaluate the effectiveness of synthesized speech. It was determined that a diphone synthesis system was the most effective choice for the scope of this project. A method of automatically identifying and extracting diphones from prompted speech was designed, allowing for the creation of a diphone database by a speaker in less than 40 minutes. CMUdict was used to determine the pronunciation of known words. A system for smoothing the transitions between diphone recordings was designed and implemented. CMUdict was then used to train a maximum-likelihood prediction system to determine the correct pronunciation of unknown English language alphabetic words. Then, a Part Of Speech tagger was designed to find the lexical class of words within a sentence.   A method of altering the pitch, duration, and volume of the produced voice over time was designed, being a combination of the TD-PSOLA algorithm and a novel approach referred to as Unvoiced Speech Duration Shifting. This minimises distortion of the voice when shifting the pitch or duration, while maximising computational efficiency by operating in the time domain. This approach was used to add correct lexical stress to vowels within words. A text tokenisation system was developed to handle arbitrary text input, allowing pronunciation of numerical input tokens and use of appropriate pauses for punctuation. Methods for further improving sentence level speech naturalness were discussed. Finally, the system was tested with listeners for its intelligibility and naturalness.
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PDF链接：
https://arxiv.org/pdf/1709.07552

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关键词：Architecture cancellation localization Modification Applications project 操作 方法 语音 used