Building the next generation of computational psycholinguistic models of speech perception

构建下一代语音感知计算心理语言学模型

• 批准号: RGPIN-2022-04431
• 负责人: Dunbar, Ewan
• 金额: $ 2.11万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753648
• 关键词: Building; next; generation; computational; psycholinguistic

How do human beings perceive and understand speech so effortlessly? Our brains give us the illusion that the process is simple, but the often strange errors made by even the most advanced artificial intelligence systems, and the baffling difficulty we have even taking stock of what we hear when we hear an unfamiliar language are clear clues that the process is in fact not easy at all. Incredibly, infants' perception becomes specialized in the language(s) they hear at home well before they can speak: as early as six months, a time when experiments also demonstrate that they recognize and understand the meaning of dozens or hundreds of common words. The cognitive science of speech perception has greatly advanced our understanding of how human speech perception works, and, to a lesser extent, of how the ability develops in infants. Nevertheless, our understanding is still very far from being advanced enough to build "human-like" computer systems that learn and perceive speech, and our current speech technology, tuned on implausibly large quantities of data, behave in many ways very differently from human beings. We seek to take advantage of recent advances in machine learning and speech technology to advance our understanding of (1) learning: what kind of systems can do the work of the infant brain and autonomously learn to decode the speech signals they hear into individual consonant and vowel sounds (currently we know of none)? do these systems end up making the same kinds of misperception errors as human listeners? (2) the early stages of auditory processing: many new speech processing systems appear, at first glance, to behave much more like the human auditory system than previous generations of speech technology, but further experiments with human listeners are needed to assess this, and to understand the implications for our understanding of human auditory processing if it is true; and, (3), how speech sounds are encoded by the brain in our memory for words. The answers to these questions have consequences for our understanding of how humans decode speech, how we learn to do this at an early age, and how we can build artificial intelligence systems that are less fragile, and that are capable of operating in far more of the world's languages.

人类如何毫不费力地感知和理解讲话？我们的大脑使我们的幻想是简单的幻想，但是即使是最先进的人工智能系统造成的奇怪错误，而且令人困惑的困难我们甚至在听到一种不熟悉的语言时听到的声音清楚的线索清楚地表明，这一过程实际上并不容易。令人难以置信的是，婴儿的感知变得专门从事他们在家里听到的语言：早在六个月时，实验也证明了他们认识并理解数十个或数百个常见单词的含义。言语感知的认知科学极大地提高了我们对人类言语感知如何运作的理解，并在较小程度上对婴儿的能力如何发展。然而，我们的理解远非足够先进，可以构建学习和感知语音的“人类般”计算机系统，而我们当前的语音技术对大量的数据进行了调整，与人类的行为不同。我们试图利用机器学习和语音技术的最新进展来提高我们对（1）学习的理解：哪种系统可以完成婴儿大脑的工作，并自主学习将听到的语音信号解码为单个辅音和元音声音（目前我们不知道）？这些系统最终会犯与人类听众一样的误解？ （2）听觉处理的早期阶段：乍看之下，许多新的语音处理系统的行为比前几代语音技术更像人类的听觉系统，但是需要对人类听众进行进一步的实验来评估这一点，并了解我们对人类听觉处理的理解（如果是真实的）的影响； （3），语音如何被我们的记忆中的大脑编码。这些问题的答案对我们对人类如何解码语音，我们如何学会在很小的时候进行此操作以及如何构建人工智能系统的理解产生了后果，这些系统如何构建不那么脆弱的人工智能系统，并且能够在世界上更多的语言中运作。