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）和语音搜索（Android）。这些系统支持通过语音对高资源语言（HRLS）（如英语、法语、普通话、日语和西班牙语）的信息访问进行重大改进。对于这些hrl，研究人员已经构建了字典、解析器、词性标注器、语言模型、搜索引擎和机器翻译引擎来支持语音技术。然而，世界上大约有6500种语言，包括他加禄语、泰米尔语、斯瓦希里语、越南语和普什图语，其中许多语言被数百万人使用，但它们没有获得构建SDS所需的计算资源。这些被称为低资源语言（LRLs）。lrl的讲话者不享有hrl讲话者所享有的相同的交流和搜索能力。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，以在这些语言中为SDS生成类似siri的语音。目前正在开发TTS合成的新范例，这使得理论上可以快速而廉价地构建系统，而无需使用为训练语音识别器等其他目的而记录的数据记录大型专用语音语料库。这项探索性研究的早期拨款调查了使用这些技术为LRL生产TTS系统。将探讨三个主要问题：1)能否开发自动技术来过滤发现的数据（例如，去除太大声、太嘈杂或不流畅的数据），以获得可理解且听起来自然的结果？2)人们能否从在线资源中获得语音词典，并通过众包验证，足以生成可理解的自然语音？3)是否可以在发现的数据上使用聚类技术来识别音高轮廓，这些音高轮廓可以在没有语言音系先验知识的情况下，通过众包来识别意义，例如问题与陈述轮廓？这些方法在两种语言上进行了测试：标准美式英语，以快速开发技术，以及在书写系统和音系上相似的立陶宛语，以评估初始LRL。这两种评估都是在可理解性和自然性方面进行的，使用了与每种语言的母语人士一起使用的众包技术。这项探索性工作的最终目标将是在各种各样的LRLs上测试这些技术，这些LRLs已被收集用于开发语音识别器。

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.