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)。 这些系统支持通过语音对英语、法语、普通话、日语和西班牙语等高资源语言 (HRLS) 进行信息访问的重大改进。 对于这些 HRL，研究人员构建了词典、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，世界上有约 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，其中许多语言有数百万人使用，但它们不具备构建 SDS 所需的计算资源。这些被称为低资源语言（LRL）。 LRL 的发言者不享有与 HRL 的发言者相同的通信和搜索功能。 特别是，支持开发文本转语音合成 (TTS) 系统以在这些语言中为 SDS 生成类似 Siri 的语音的研究和资源很少。目前正在开发 TTS 合成的新范例，这在理论上使其成为可能快速、廉价地构建系统，而无需使用为其他目的（例如训练语音识别器）记录的数据来记录大型专用语音语料库。 这项早期探索性研究资助调查了如何使用这些技术来为 LRL 生成 TTS 系统。 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获得发音词典，并通过众包验证足以生成可理解且自然的语音？ 3) 人们是否可以对找到的数据使用聚类技术来识别音调轮廓，这些音调轮廓可以通过众包来识别含义，例如问题与陈述轮廓，而无需先了解一种语言的音韵学？ 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；以及一种在书写系统和音韵学方面类似的语言，立陶宛语，以评估初始 LRL。这两项评估都是通过众包技术与每种语言的母语人士的可理解性和自然度进行的。 这项探索性工作的最终目标是在各种 LRL 上测试这些技术，这些 LRL 是为了开发语音识别器而收集的。