Collaborative Research: CompCog: Psychological, Computational, and Neural Adequacy in a Deep Learning Model of Human Speech Recognition

合作研究：CompCog：人类语音识别深度学习模型中的心理、计算和神经充分性

• 批准号: 2043950
• 负责人: Kevin Brown
• 金额: $ 17.89万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2043950&HistoricalAwards=false
• 关键词: Collaborative; Research; CompCog; Psychological; Computational

Computer technology for speech recognition has advanced to an amazing degree over the past decade. Many of us use it daily -- to dictate text messages on smart phones or to navigate automated phone systems. As good as these systems are, humans still outperform them in complex, crowded, and noisy acoustic environments. If more were known concerning how humans adapt to these challenging situations, speech technology might be made more adaptive and robust. For example, computer systems for speech recognition use complex "deep learning" networks that often need to be trained in ways that are very different from how humans learn language. Although neural network models aimed at simulating human language processing are much simpler, which allows scientists to develop hypotheses about how human language processing works, they don't use real speech as input. Instead, they use phonetic features that are more like text than speech and so fail to address the core problem of how humans map the acoustics of speech to words. This research program focuses on bridging the gap between the complex artificial neural network models used in current technologies for speech recognition and the simpler neural network models used to investigate how humans actually perceive speech.This research program builds on a new neural network model for speech that aims to achieve high recognition accuracy on many words produced by several speakers. Crucially, the model can do this with minimal complexity (using many fewer layers than commercial speech recognition systems), which allows researchers to understand the computations it performs. The research plans include extending the model to a large vocabulary, training on naturalistic speech, and adding biologically plausible preprocessing modeled on the human auditory pathways. The model will be compared with key aspects of human spoken word recognition behavior as well as with human neural responses to spoken speech. The work has the potential to generate new insights to advance speech technology by making it more robust in challenging environments, with potential impact on speech technology used for health, law, education, and the automatic captioning that makes speech accessible to the deaf and hard of hearing. In addition, individuals ranging from high school students to Ph.D. students will be part of the research team and will have rich research experiences that will promote development of technical skills useful for careers in academic research or a variety of non-academic careers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的十年中，用于语音识别的计算机技术已达到了惊人的学位。我们中的许多人每天都使用它 - 指示智能手机上的短信或导航自动电话系统。就像这些系统一样好，人类仍然在复杂，拥挤和嘈杂的声学环境中胜过它们。如果有更多关于人类如何适应这些具有挑战性的情况的知情，那么语音技术可能会变得更加适应性和强大。例如，语音识别的计算机系统使用复杂的“深度学习”网络，这些网络通常需要以与人类学习语言有很大不同的方式进行培训。尽管旨在模拟人类语言处理的神经网络模型要简单得多，这使科学家可以对人类语言处理的工作方式提出假设，但他们不使用真实的语音作为输入。取而代之的是，他们使用的语音功能更像文本而不是语音，因此无法解决人类如何将语音声学映射到单词的核心问题。该研究计划的重点是弥合当前技术中用于语音识别技术的复杂人工神经网络模型与用于研究人类实际上如何感知语音的更简单的神经网络模型之间的差距。该研究计划基于一种新的语音神经网络模型，用于语音的新神经网络模型，该模型以此为其介绍的语音模型。旨在在许多演讲者产生的许多单词上实现高认识的准确性。至关重要的是，该模型可以以最小的复杂性（使用比商业语音识别系统少得多的层）来做到这一点，从而使研究人员能够理解其执行的计算。研究计划包括将模型扩展到大型词汇，自然主义语音的培训，并添加在人类听觉途径上建模的生物学上合理的预处理。该模型将与人类口头识别行为的关键方面以及人类对口语的神经反应进行比较。这项工作有可能通过在具有挑战性的环境中提高言语技术来产生新的见解，从而对健康，法律，教育和自动字幕的潜在影响，这使聋哑人的语音可访问和困难听力。此外，从高中生到博士学位的个人不等。学生将成为研究团队的一部分，并拥有丰富的研究经验，将促进对学术研究或各种非学术职业有用的技术技能的发展。该奖项反映了NSF的法定任务，并被认为值得通过使用基金会的智力优点和更广泛的影响评估标准进行评估。

项目成果

A tale of two lexica: Investigating computational pressures on word representation with neural networks.

• DOI: 10.3389/frai.2023.1062230
• 发表时间: 2023
• 期刊: FRONTIERS IN ARTIFICIAL INTELLIGENCE
• 影响因子: 4
• 作者: Avcu, Enes;Hwang, Michael;Brown, Kevin Scott;Gow, David W.
• 通讯作者: Gow, David W.

Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations

• DOI: 10.1111/cogs.13291
• 发表时间: 2023-05-01
• 期刊: COGNITIVE SCIENCE
• 影响因子: 2.5
• 作者: Brown，Kevin S.;Yee，Eiling;McRae，Ken
• 通讯作者: McRae，Ken