Computer-Based Pronunciation Analysis for Children with Speech Sound Disorders

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言语产生评估和发音训练工具。计算机辅助发音训练的研究成果尚未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。本探索性研究的目标是开发一套算法，这些算法将构成有效的语音障碍儿童语音分析系统的核心组成部分。该系统的组成部分，当协同使用时，将可靠地识别和评分一个孤立的目标单词中的音素的可理解性。该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/）。所提出的工作所产生的工具将提供关于发音错误的即时、相关和可理解的反馈。具体目标是：(1)创建个性化的语音模板，用于客观分析语音；(2)自动识别语音录音中的音素位置；(3)自动为语音障碍儿童进行音素可理解性评分。对于具体目标1，评估参与者口语单词的模板将从该单词的大量模板中选择，每个模板将进一步个性化以匹配参与者的一般频谱特征。对于Specific Aim 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识别可能的差异，观察和预期音素序列使用几个独立的信息来源。方法将包括使用隐马尔可夫模型的发音方式自动分类，动态时间翘曲，以及可能的音素错误的先验确定。具体目标3将提供目标音素的可理解性的衡量标准，并识别扭曲的特征。可理解性评分将使用音素可理解性分析（PIA）模块进行，该模块是音素特定的，由六个信息源组成，包括目标音素的声学模板、可能的语音替换、分析中使用的声学特征、可接受阈值、给定上下文中音素持续时间的统计和评估指标。使用人类感知数据（可理解性分数）作为训练数据是该方法的重要组成部分。

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.