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言语发音障碍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研究产生的技术尚未成功推广到帮助患有语音障碍的儿童，这主要是因为对语音信号的音素水平分析缺乏准确性。拟议的探索性研究的目标是开发一套算法，这些算法将构成语音障碍儿童有效发音分析系统的核心组件。当这个系统的组件配合使用时，将可靠地识别和评分孤立目标单词中的音素的可理解性。算法还将识别特定类型的失真错误(例如，前置，其中/sh/音素实现为/S/)。建议的工作产生的工具将提供有关发音错误的即时、相关和可理解的反馈。其具体目标是(1)创建用于发音客观分析的个性化语音模板，(2)自动识别语音记录中的音素位置，以及(3)为语音障碍儿童自动评分音素清晰度。对于具体目标1，将从一个大的单词模板池中选择用于评估参与者的口语单词的模板，并且每个模板将进一步个性化以匹配参与者的一般频谱特征。对于特定的目标2，主要的挑战是当观察到的(说出的)音素序列不同于预期的(目标)音素序列时识别音素位置。将使用几个独立的信息来源，通过五个步骤来确定观察到的音素序列和预期音素序列之间的可能差异。方法将包括使用隐马尔可夫模型对发音方式进行自动分类、动态时间弯曲以及对可能的音素错误进行先验确定。特定目标3将提供对目标音素的可理解性的测量，并识别失真特征。将使用拟议的音素可理解性分析模块进行可理解性评分，该模块针对特定音素，由六个信息源组成，包括目标音素的声学模板、可能的语音替代、分析中使用的声学特征、可接受性阈值、特定语境中音素持续时间的统计数据和评价指标。使用人类感知数据(可理解性分数)作为训练数据是所提出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相关，因为这项工作产生的软件工具将使患有语音障碍的儿童能够更好地与普通民众交流。此外，这些工具将帮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他们的时间。

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.