Biofeedback-Enhanced Treatment for Speech Sound Disorder: Randomized Controlled Trial and Delineation of Sensorimotor Subtypes

项目总结/摘要 言语声音障碍的儿童在口语交流中表现出准确性和可理解性下降， 因此，可能被认为不如同龄人有能力或聪明，对社会和经济都有负面影响。 情感和社会经济结果[1]-[3]。虽然大多数言语错误在学龄后期得到解决， 10 - 15岁，2-5%的说话者表现出残留语音障碍（RSSD），持续到青春期。 [4]，[5]，反映了美国约600万例。在一系列的实验研究中 自2013年以来，我们的研究团队已经证明，采用技术强化饲料的治疗， 背部可以改善对先前干预没有反应的RSSD患者的言语产生， [6]-[10]。母公司奖的主要目标（R 01 DC 017476，“生物反馈-强化治疗 言语声音障碍”）是进行第一个有效的随机对照试验， 对于最常见的RSSD类型（英语/r/发音错误），进行了理论与生物反馈干预。 RSSD的治疗也可以通过开发结合人工免疫的工具来加强。 智能/机器学习（AI/ML）。具有语音自动评分的应用程序原则上可以 用于增强临床医生服务，并实现更高强度的实践，以加快进展。但没有 迄今为止，计算机化治疗已经证明对于儿童的临床使用具有足够的准确性[11]。现有 系统的局限性主要在于公开可用的语音语料库很少表示 儿童或有语言障碍的人。这种数据稀缺是一个根本问题， 自动语音识别（ASR）的临床应用进展[12]。建议的sup- Plement将通过修改和增强PERCEPT（临床感知错误评级）来解决这一障碍， 语音目标的临床评估），一个现有的儿童语音录音语料库 通过父母奖和研究人员自2013年以来的NIH资助的研究。AI/ML应用 预计扩大后的数据库将产生双重科学影响。首先，我们预期直接收益 对于患有影响/r/的RSSD的儿童，其语音样本构成当前语料库的大部分。我们将 使用我们的数据库来训练神经网络，以将包含/r/的新颖子产品分类为正确或不正确。 rect.然后可以将此分类器纳入AI工具中，以提高儿童干预的有效性 关于RSSD其次，我们预计，工程师们将致力于研究儿童或临床ASR的更广泛问题， 语音将对使用PERCEPT进行模型训练感兴趣，特别是在语料库被增强之后， 更多样的数据，如这里所提出的。我们希望表明，用PERCEPT生成的声学模型可以 提高当前可用的开放访问语音识别系统的性能（例如，Kaldi [13]）在 识别新颖的儿童言语刺激。PERCEPT数据库将通过以下部分向公众开放： 与PhonBank合作，PhonBank是NIH资助的语音研究数据共享平台（例如，[14]-[16]）。

Project Summary/Abstract Children with speech sound disorder show diminished accuracy and intelligibility in spoken communication and may thus be perceived as less capable or intelligent than peers, with negative consequences for both socio- emotional and socioeconomic outcomes [1]–[3]. While most speech errors resolve by the late school-age years, between 2-5% of speakers exhibit residual speech sound disorder (RSSD) that persists through adoles- cence or even adulthood [4], [5], reflecting about 6 million cases in the US. In a series of experimental studies since 2013, our research team has demonstrated that treatment incorporating technologically-enhanced feed- back can improve speech production in individuals with RSSD who have not responded to previous interven- tion [6]–[10]. The primary objective of the parent award (R01 DC017476, “Biofeedback-Enhanced Treatment for Speech Sound Disorder”) is to conduct the first well-powered randomized controlled trial comparing tradi- tional vs biofeedback intervention for the most common type of RSSD, misarticulation of the English /r/ sound. Treatment of RSSD could also be enhanced through the development of tools incorporating artificial intelligence/machine learning (AI/ML). Applications with automated scoring of speech sounds could in principle be used to augment clinician services and achieve higher-intensity practice for faster progress. However, no computerized treatment to date has demonstrated sufficient accuracy for clinical use with children [11]. Existing systems are limited primarily by the fact that publicly available speech corpora have very little representation of either children or individuals with speech impairments. This data scarcity represents a fundamental issue hin- dering advances in clinical applications of automatic speech recognition (ASR) [12]. The proposed sup- plement will address this barrier by modifying and augmenting PERCEPT (Perceptual Error Rating for the Clin- ical Evaluation of Phonetic Targets), an existing corpus of acoustic recordings of child speech assembled through the parent award and the investigators’ previous NIH-funded research since 2013. AI/ML applications of the augmented database are expected to have a twofold scientific impact. First, we anticipate direct benefits for children with RSSD affecting /r/, whose speech samples make up the majority of the current corpus. We will use our database to train a neural network to classify novel child productions containing /r/ as correct or incor- rect. This classifier could then be incorporated into AI tools to increase the efficacy of intervention for children with RSSD. Second, we anticipate that engineers working on the broader problem of ASR for child or clinical speech will be interested in using PERCEPT for model training, especially after the corpus is augmented with more diverse data, as proposed here. We expect to show that acoustic models generated with PERCEPT can improve the performance of currently available open-access speech recognition systems (e.g., Kaldi [13]) in recognition of novel child speech stimuli. The PERCEPT database will be made publicly available through part- nership with PhonBank, an NIH-funded data-sharing platform for speech research (e.g., [14]–[16]).