Computer-Based Pronunciation Analysis for Children with Speech Sound Disorders

基于计算机的语音障碍儿童发音分析

• 批准号: 8227504
• 负责人: Alexander Kain
• 金额: $ 22.71万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-21 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8227504
• 关键词: Acoustics; Adult; Affect; Algorithms; American Speech-Language-Hearing Association; Characteristics; Child; Classification; Communication; Complement; Computer Assisted; Computers; Data; Data Set; Diagnosis; Disease; Evaluation; Feedback; General Population; Genetic Transcription; Goals; Human; Individual; Joints; Language; Location; Manuals; Measures; Methods; Metric; Nursery Schools; Output; Participant; Pathologist; Performance; Phonetics; Population; Probability; Process; Production; Public Health; Reading; Research; School-Age Population; Schools; Signal Transduction; Software Tools; Source; Speech; Speech Sound; System; Systems Analysis; Techniques; Technology; Testing; Time; Training; Work; Writing; base; disability; innovation; markov model; mathematical ability; peer; phonology; remediation; spelling; statistics; success; teacher; tool

DESCRIPTION (provided by applicant): The long-term objective of the proposed work is to develop speech-production assessment and pronunciation- training tools for children with speech sound disorders. The technology resulting from research on computer- assisted pronunciation training has not yet been successfully extended to help children with speech sound disorders, primarily because of a lack of accuracy in phoneme-level analysis of the speech signal. The goal of the proposed exploratory research is to develop a set of algorithms that will constitute the core components of an effective pronunciation analysis system for children with speech sound disorders. The components of this system, when used in concert, will reliably identify and score the intelligibility of a phoneme within an isolated target word. The algorithms will also identify specific types of distortion errors (e.g. fronting, in which the /sh/ phoneme is realized as /s/). The tools resulting from the proposed work will provide immediate, relevant, and understandable feedback about pronunciation errors. The Specific Aims are to (1) Create individualized speech templates for use in objective analysis of pronunciation, (2) Automatically identify phoneme locations in speech recordings, and (3) Automatically score phoneme intelligibility for children with speech sound disorders. For Specific Aim 1, the template for evaluating a participant's spoken word will be selected from a large pool of templates of that word, and each template will be further individualized to match the general spectral characteristics of the participant. For Specific Aim 2, the primary challenge is to identify phoneme locations when the observed (spoken) phoneme sequence is different from the expected (target) phoneme sequence. A five-step process will be used to identify possible differences between the observed and expected phoneme sequence using several independent sources of information. Methods will include automatic classification of manner of articulation using a Hidden Markov Model, dynamic time warping, and a priori determination of likely phoneme errors. Specific Aim 3 will provide a measure of the intelligibility of a target phoneme and also identify distorted features. The scoring of intelligibility will be performed using a proposed Phoneme Intelligibility Analysis (PIA) module, which is phoneme-specific and composed of six sources of information, including an acoustic template of the target phoneme, likely phonetic substitutions, acoustic features used in analysis, thresholds of acceptability, statistics of phoneme duration in the given context, and evaluation metrics. The use of human perceptual data (intelligibility scores) as training data is an important and new component of the proposed approach. PUBLIC HEALTH RELEVANCE: The proposed work is relevant to the public health in that the software tools that result from this work will enable children with speech sound disorders to better communicate with the general population. Furthermore, these tools will assist teachers of such children in the task of pronunciation assessment, allowing the teachers to more effectively use their time.

描述（由申请人提供）：拟议工作的长期目标是为有语音障碍的儿童开发语音产生评估和发音训练工具。从计算机辅助发音训练的研究中得到的技术还没有成功地扩展到帮助有语音障碍的儿童，主要是因为语音信号的音素级分析缺乏准确性。建议的探索性研究的目标是开发一套算法，将构成一个有效的语音分析系统的核心组成部分，为儿童语音障碍。该系统的组成部分，当在音乐会上使用时，将可靠地识别和评分的一个孤立的目标词内的音素的可理解性。算法还将识别特定类型的失真错误（例如前置，其中/sh/音素被实现为/s/）。从拟议的工作产生的工具将提供即时的，相关的，可理解的发音错误的反馈。具体目标是（1）创建个性化的语音模板，用于发音的客观分析，（2）自动识别语音记录中的音素位置，以及（3）自动为语音障碍儿童的音素清晰度评分。对于特定目标1，用于评估参与者的口语的模板将从该单词的大量模板池中选择，并且每个模板将被进一步个性化以匹配参与者的一般频谱特性。对于具体目标2，主要的挑战是当观察到的（口语）音素序列与预期的（目标）音素序列不同时识别音素位置。一个五步的过程将被用来确定观察到的和预期的音素序列之间可能存在的差异，使用几个独立的信息源。方法将包括使用隐马尔可夫模型，动态时间弯曲，和可能的音素错误的先验确定的发音方式的自动分类。具体目标3将提供目标音素的可懂度的量度，并且还识别失真的特征。可懂度的评分将使用建议的音素可懂度分析（PIA）模块，这是音素特定的，由六个信息源，包括目标音素的声学模板，可能的语音替代，分析中使用的声学特征，可接受性阈值，在给定的上下文中的音素持续时间的统计，和评估指标。使用人类的感知数据（可懂度分数）作为训练数据是所提出的方法的一个重要和新的组成部分。 公共卫生相关性：拟议的工作与公共卫生有关，因为这项工作产生的软件工具将使有语音障碍的儿童能够更好地与普通人群交流。此外，这些工具将协助这些儿童的教师进行发音评估，使教师能够更有效地利用时间。