Computer-Based Pronunciation Analysis for Children with Speech Sound Disorders

摘要 拟议工作的长期目标是开发语音产生评估和发音- 语音障碍儿童的训练工具。由计算机研究产生的技术-- 辅助发音训练还没有成功地扩展到帮助语音障碍儿童 障碍，主要是因为语音信号的音素水平分析缺乏准确性。的目标是 拟议的探索性研究是开发一套算法，这些算法将构成 一种有效的语音障碍儿童发音分析系统。这其中的组件 当系统配合使用时，将可靠地识别和评分孤立的 目标词。算法还将识别特定类型的失真错误(例如，前置，其中/sh/ 音素实现为/S/)。建议工作产生的工具将提供即时、相关和 关于发音错误的可理解的反馈。 具体目标是(1)创建用于客观分析的个性化语音模板 发音，(2)自动识别语音记录中的音素位置，以及(3)自动评分 语音障碍儿童的音素清晰度。对于特定目标1，模板用于 评估参与者的口语单词将从该单词的大模板池中选择，每个模板 模板将进一步个性化，以匹配参与者的一般光谱特征。为 具体目标2，主要挑战是在观察到(说出)音素时识别音素位置 序列与预期的(目标)音素序列不同。将使用五个步骤的流程来 使用以下方法确定观察到的音素序列和预期音素序列之间的可能差异 独立的信息来源。方法将包括发音方式的自动分类 使用隐马尔可夫模型、动态时间规整和可能的音素错误的先验确定。 特定目标3将提供对目标音素的可理解性的测量，并且还识别失真 功能。清晰度评分将使用拟议的音素清晰度分析(PIA)进行 模块，它是特定于音素的，由六个信息源组成，包括一个声学模板 目标音素、可能的语音替换、分析中使用的声学特征、阈值 可接受性，给定语境中音素持续时间的统计，以及评估指标。人类的使用 作为训练数据的感知数据(可理解性分数)是建议的 接近。

Summary 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.