EAGER: Creating Speech Synthesizers for Low Resource Languages

EAGER：为低资源语言创建语音合成器

• 批准号: 1548092
• 负责人: Julia Hirschberg
• 金额: $ 15万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548092&HistoricalAwards=false
• 关键词: EAGER; Creating; Speech; Synthesizers; Low

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.

语音技术的最新进展导致了语音对话系统（SDS）的广泛使用，例如Siri（iPhone）和语音搜索（Android）。 这些系统支持通过高资源语言（HRLS）（如英语，法语，汉语，日语和西班牙语）的语音信息访问的重大改进。 对于这些HRL，研究人员已经建立了字典，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，但它们并不享有构建SDS所需的计算资源。这些语言被称为低资源语言（LRL）。 LRL的发言者不享有与HRL的发言者相同的通信和搜索能力。 特别是，有很少的研究和资源支持开发的文本到语音合成（TTS）系统，以产生类似的语音SDS在这些languages.TTS合成新的范例，现在正在开发，这使得它在理论上可以建立系统快速和廉价，而无需记录大型，特殊用途的语音语料库使用的数据记录用于其他目的，如培训语音识别器。 EArly的探索性研究资助旨在研究如何使用这些技术为LRL生产TTS系统。 我们将探讨三个主要问题：1）是否可以开发自动技术来过滤找到的数据（例如，删除太大声、太嘈杂或不流利的数据），以获得可理解和听起来自然的结果？2)人们能否从在线资源中获得发音词典，并通过众包验证，足以生成可理解和自然的语音？ 3)可以使用聚类技术对发现的数据来识别音高轮廓，可以众包，以确定意义，如问题与声明轮廓没有事先了解一种语言的音韵？ 这些方法在两种语言上进行测试：标准美式英语，以快速开发技术，以及在书写系统和语音学上类似的语言，立陶宛语，以评估初始LRL。这两项评估都是在可理解性和自然性方面进行的，使用的是每种语言的母语使用者的众包技术。 这项探索性工作的最终目标将是测试这些技术在各种各样的LRL已收集的目的，开发语音识别器。