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.

人类如何如此轻松地感知和理解言语？我们的大脑给我们一种错觉，认为这个过程很简单，但即使是最先进的人工智能系统也常常会犯奇怪的错误，而且当我们听到不熟悉的语言时，我们甚至难以评估我们所听到的内容，这些都是明确的线索：这个过程其实一点也不容易。令人难以置信的是，婴儿的感知能力在他们会说话之前就已经专门针对他们在家里听到的语言：早在六个月时，实验也表明他们能够识别并理解数十或数百个常见单词的含义。言语感知的认知科学极大地增进了我们对人类言语感知如何工作的理解，并且在较小程度上加深了对婴儿这种能力如何发展的理解。尽管如此，我们的理解还远远不够先进，不足以构建学习和感知语音的“类人”计算机系统，而且我们当前的语音技术依赖于令人难以置信的大量数据，在许多方面的表现与人类非常不同。我们寻求利用机器学习和语音技术的最新进展来增进我们对（1）学习的理解：什么样的系统可以完成婴儿大脑的工作并自主学习将他们听到的语音信号解码为单独的辅音和元音（目前我们还没有听说过）？这些系统最终会犯与人类听众相同的误解错误吗？ (2) 听觉处理的早期阶段：乍一看，许多新的语音处理系统的表现比前几代语音技术更像人类听觉系统，但需要对人类听众进行进一步的实验来评估这一点，并且如果这是真的，了解对我们理解人类听觉处理的影响； (3)大脑如何在我们的单词记忆中编码语音。这些问题的答案对我们理解人类如何解码语音、我们如何在很小的时候学习如何做到这一点以及我们如何构建不那么脆弱、能够在更多的环境中运行的人工智能系统产生影响。世界上的语言。