SBE-RCUK: CompCog: Modeling the Development of Phonetic Representations

SBE-RCUK：CompCog：对语音表示的发展进行建模

• 批准号: 1734245
• 负责人: Naomi Feldman
• 金额: $ 52.01万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734245&HistoricalAwards=false
• 关键词: SBE; RCUK; CompCog; Modeling; Development

Listeners' processing of speech is tuned to their native language. For example, Japanese listeners categorizing English [l] and [r] do not rely on the same aspects of the speech signal that native English listeners do. This project uses computational models to investigate how children develop language-specific perceptual strategies. A better understanding of this perceptual learning process could lead to better diagnosis and treatment of developmental language impairments that have a perceptual basis and can provide insight into the difficulties that listeners face when learning a second language in adulthood. Building computational models of how children learn their native language from the speech around them can also lead to improved speech technology for low-resource languages (languages that are not spoken by many people in the world or that lack digital resources such as large-scale, annotated databases), ultimately leading to systems that learn more effectively using little or no transcribed audio. Such systems could become important tools for documenting and analyzing endangered and minority languages and could help make speech technology more universally available.A series of simulations tests the hypothesis that children's processing of speech can become specialized for their native language through a process of dimension learning that does not rely on knowledge of sound categories. Two models that use dimension learning are proposed, drawing on representation learning methods that have performed well in low-resource automatic speech recognition, where extensive labeled training data are not available. The first model relies on temporal information as a proxy for sound category knowledge, while the second model relies on top-down information from similar words, which infants have been shown to use. Each model is trained on speech recordings from a particular language and is evaluated on its ability to predict how adults and infants with that language background discriminate sounds. The research will yield new methods for training and testing cognitive models of language with naturalistic speech recordings and has the potential to significantly impact theories of how and when children learn about the sounds of their native language.

听者对语音的处理是根据他们的母语进行的。例如，将英语归类为[L]和[r]的日本听者并不像本族英语听者那样依赖言语信号的相同方面。这个项目使用计算模型来研究儿童如何发展特定于语言的感知策略。更好地了解这一知觉学习过程可以更好地诊断和治疗具有知觉基础的发展性语言障碍，并有助于洞察听者在成年后学习第二语言时所面临的困难。建立儿童如何通过他们周围的语音学习母语的计算模型也可以改进针对低资源语言(世界上许多人不说的语言或缺乏数字资源，如大型注释数据库)的语音技术，最终导致使用很少或没有转录音频的系统更有效地学习。这类系统可能成为记录和分析濒危和少数民族语言的重要工具，并有助于使语音技术更普遍地可用。一系列模拟测试了一种假设，即儿童通过不依赖于声音类别知识的维度学习过程，可以对其母语进行专门的语音处理。提出了两个使用维度学习的模型，借鉴了在低资源自动语音识别中表现良好的表示学习方法，在低资源的自动语音识别中，大量的标签训练数据不可用。第一个模型依赖于时间信息作为声音类别知识的代理，而第二个模型依赖于来自相似单词的自上而下的信息，婴儿已经被证明使用过这些信息。每个模型都根据特定语言的语音录音进行训练，并根据其预测具有该语言背景的成年人和婴儿如何辨别声音的能力进行评估。这项研究将为用自然语言录音训练和测试语言认知模型提供新的方法，并有可能对儿童如何以及何时学习母语声音的理论产生重大影响。

项目成果

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input

• DOI: 10.1073/pnas.2001844118
• 发表时间: 2021-02-16
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Schatz，Thomas;Feldman，Naomi H.;Dupoux，Emmanuel
• 通讯作者: Dupoux，Emmanuel

Modeling Rhythm in Speech as in Music: Towards a Unified Cognitive Representation

像音乐一样对语音节奏进行建模：迈向统一的认知表征

• 发表时间: 2022
• 期刊: Proceedings of the Conference on Cognitive Computational Neuroscience
• 作者: Li, Ruolan;Schatz, Thomas;Feldman, Naomi H.
• 通讯作者: Feldman, Naomi H.

Input matters in the modeling of early phonetic learning

输入在早期语音学习建模中很重要

• 发表时间: 2020
• 期刊: Proceedings of the Annual Conference of the Cognitive Science Society
• 作者: Li, Ruolan;Schatz, Thomas;Matusevych, Yevgen;Goldwater, Sharon;Feldman, Naomi H.
• 通讯作者: Feldman, Naomi H.

Infant Phonetic Learning as Perceptual Space Learning: A Crosslinguistic Evaluation of Computational Models

• DOI: 10.1111/cogs.13314
• 发表时间: 2023-07
• 期刊: Cognitive science
• 影响因子: 2.5
• 作者: Yevgen Matusevych;Thomas Schatz;H. Kamper;Naomi H Feldman;S. Goldwater

A quantitative model of the language familiarity effect in infancy

• DOI: 10.32470/ccn.2019.1353-0
• 发表时间: 2019
• 期刊: 2019 Conference on Cognitive Computational Neuroscience
• 作者: Craig A. Thorburn;Naomi H Feldman;Thomas Schatz