Modeling Pronunciation Variation for Universal Access to Speech Understanding

为普遍获得语音理解而建模发音变化

• 批准号: 9978025
• 负责人: Daniel Jurafsky
• 金额: $ 50.4万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9978025&HistoricalAwards=false
• 关键词: Modeling; Pronunciation; Variation; Universal; Access

In order for speech-based interfaces to be universally accessible - that is, available to all citizens in all situations - they must deal successfully with massive variability in pronunciation, as studies have shown that this is by far the single largest source of error in machine speech recognition. This project will address that issue by building dynamic pronunciation models which use context to predict the most likely pronunciations of a word in a particular utterance. The PIs' previous work has established that speakers are more likely to delete certain sounds (e.g., the final "t" in "about") in a variety of situations: if the next word starts with certain consonants; if they are speaking quickly; if the word is unsurprising (has a high trigram probability); if they are young and male; if the word is not followed by a pause or a word-repetition; or if they are speakers of certain dialects. They have further shown how confidence metrics can be used to identify inaccurate dictionary pronunciations based on actual pronunciations in speech. This project will extend and combine both of these lines of research, by using phonetic-confidence models to identify words with incorrect pronunciations that contribute to word-error, and then building statistical (decision-tree) models of pronunciation variation for these words. The PIs' model provides a general engine which they will apply to several specific classes of pronunciation variation, including dialect and speaker type, embedded in the Sphinx-II speech recognizer. The application of computational models to the study of pronunciation variation is an important new research area in linguistics and cognitive modeling, with implications for and potential benefits to many areas of science and pedagogy, including universal access.

为了使基于语音的界面可以普遍访问 - 在所有情况下，所有公民都可以使用 - 他们必须成功处理发音的巨大可变性，因为研究表明，这是迄今为止机器语音识别中最大的错误源。 该项目将通过构建动态发音模型来解决该问题，该模型使用上下文来预测特定话语中单词最可能的发音。 PIS先前的工作已经确定，在各种情况下，说话者更有可能删除某些声音（例如，最终的“ t”）：如果下一个单词以某些辅音开头； 如果他们说话很快； 如果这个词毫不奇怪（较高的trigram概率）； 如果他们年轻和男性； 如果该单词不紧随其后，则是暂停或单词重复； 或者，如果他们是某些方言的说话者。 他们进一步展示了如何使用置信度指标来根据语音中的实际发音来识别不准确的字典发音。该项目将通过使用语音信心模型来识别具有不正确的发音的单词来扩展和结合这两个研究线，然后构建这些单词发音变化的统计（决策树）模型。 PIS的模型提供了一个通用引擎，它们将应用于嵌入Sphinx-II语音识别器中的几种特定类别的发音变化类别，包括方言和扬声器类型。 计算模型在发音差异研究中的应用是语言学和认知建模的重要新研究领域，对许多科学和教学法的影响和潜在的好处，包括普遍访问。