EAGER: Creating Speech Synthesizers for Low Resource Languages

最近语音技术的进步导致了语音对话系统(SDS)的广泛使用，如Siri(IPhone)和Voice Search(Android)。这些系统支持英语、法语、普通话、日语和西班牙语等高级资源语言(HRLS)的语音信息访问方面的重大改进。对于这些HRL，研究人员已经构建了词典、解析器、词性标记器、语言模型、搜索引擎和机器翻译引擎来支持语音技术。然而，世界上有大约6500种语言，包括他加洛语、泰米尔语、斯瓦希里语、越南语和普什图语，其中许多语言有数百万人说，但它们并不享受构建SD所需的计算资源。这些被称为低资源语言(LRL)。LRL的说话者不享有与HRL的说话者相同的通信和搜索能力。特别是，很少有研究和资源支持开发文本到语音合成(TTS)系统来产生用于这些语言的SD的类似Siri的语音。现在正在开发用于TTS合成的新范例，其使得在理论上有可能快速且廉价地建立系统，而无需使用为诸如训练语音识别器等其他目的而记录的数据来记录大型专用语音语料库。这项早期的探索性研究基金调查了使用这些技术为LRL生产TTS系统的情况。将探讨三个主要问题：1)人们能否开发自动技术来过滤发现的数据(例如，移除太吵、太吵或不流利的数据)，以获得可理解和听起来自然的结果？2)人们能否从在线来源获得发音词典，通过众包验证，足以产生可理解和自然的语音？3)人们能否在发现的数据上使用聚类技术来识别基音轮廓，这些轮廓可以在不需要事先了解一种语言的音系学的情况下，通过众包来识别诸如疑问句轮廓之类的含义？这些方法在两种语言上进行了测试：标准的美国英语，以迅速发展技术，以及一种在书写系统和语音方面相似的语言，立陶宛语，以评估最初的LRL。这两种评估都是在可理解性和自然性方面进行的，使用了以每种语言为母语的众包技术。这项探索性工作的最终目标将是在广泛的LRL上测试这些技术，这些LRL是为开发语音识别器而收集的。

Recent advances in speech technology have resulted in wide use of Spoken Dialogue Systems (SDS) such as Siri (iPhone) and Voice Search (Android). These systems support major improvements in information access by voice for High Resource Languages (HRLS) such as English, French, Mandarin, Japanese, and Spanish. For these HRLs, researchers have built dictionaries, parsers, part-of-speech taggers, language models, search engines, and machine translation engines to support speech technologies. However, there are ~6500 world languages, including Tagalog, Tamil, Swahili, Vietnamese and Pashto, many of which are spoken by millions of people, but which do not enjoy the computational resources necessary to build SDS. These are termed Low Resource Languages (LRLs). Speakers of LRLs do not enjoy the same communication and search capabilities speakers of HRLs do. In particular, there is little research and few resources supporting the development of Text-to-Speech Synthesis (TTS) systems to produce Siri-like speech for SDS in these languages.New paradigms for TTS synthesis are now being developed which make it theoretically possible to build systems quickly and cheaply without recording large, special-purpose speech corpora using data recorded for other purposes such as training speech recognizers. This EArly Grant for Exploratory Research investigates the use of these techniques to produce TTS systems for LRL. Three major problems will be explored: 1) Can one develop automatic techniques to filter found data (removing data that is too loud, too noisy or disfluent, for example) to obtain intelligible and natural-sounding results? 2) Can one obtain pronunciation dictionaries from online sources that, with crowd-sourced validation, suffice to generate intelligible and natural speech? 3) Can one use clustering techniques on found data to identify pitch contours that can be crowd-sourced to identify meanings such as question vs. statement contours without prior knowledge of a language's phonology? These methods are tested on two languages: Standard American English, to develop the techniques rapidly, and a language similar in writing system and phonology, Lithuanian, to evaluate on an initial LRL. Both evaluations are made in terms of intelligibility and naturalness using crowd-sourcing techniques with native speakers of each language. The ultimate goal of this exploratory work will be to test these techniques on a broad variety of LRLs which have been collected for purposes of developing speech recognizers.