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

项目摘要/摘要 患有语音障碍的儿童在口语交流中表现出准确性和可理解性降低，并 因此可能被认为能力或智力低于同龄人，对社会和社会造成负面影响 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就能解决 几年来，2%-5%的说话者表现出持续整个青春期的残留语音障碍(RSSD)- 甚至成年[4]、[5]，反映了美国约600万个病例。在一系列实验研究中 自2013年以来，我们的研究团队已经证明，采用技术增强的饲料- BACK可以改善患有RSSD但对之前的干预没有反应的人的言语产生。 Ten[6]-[10]。家长奖(R01 DC017476)的主要目标是“生物反馈--强化治疗 用于语音障碍)将进行第一个有效的随机对照试验，比较传统- 对最常见类型的RSSD，英语/r/发音发音错误的条件反馈与生物反馈干预。 RSSD的治疗也可以通过开发结合人工合成的工具来加强 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 用于加强临床医生服务，实现更高强度的实践，以获得更快的进展。然而，没有 到目前为止，计算机化的治疗已经证明了足够的准确性，可以用于儿童的临床[11]。现有 这些系统主要受到以下事实的限制：公开可用的语音语料库很少代表 无论是儿童还是有语言障碍的个人。这种数据稀缺代表了一个根本性的问题- 自动语音识别(ASR)的临床应用进展[12]。建议的建议是- Plement将通过修改和增强Percept(临床的感知错误评级)来解决这一障碍 语音目标的语音评估)，现有的儿童语音的声音记录语料库 通过父母奖和研究人员自2013年以来由NIH资助的先前研究。AI/ML应用 扩大的数据库预计将产生双重的科学影响。首先，我们期待直接的利益。 对于影响/r/的患有RSSD的儿童，其语音样本占当前语料库的大部分。我们会 使用我们的数据库来训练神经网络，以将包含/r/的新子产品分类为正确或有害- 直立。然后，可以将该分类器整合到人工智能工具中，以提高对儿童的干预效果 使用RSSD。其次，我们预计工程师将致力于儿童或临床ASR这一更广泛的问题 语音将有兴趣使用感知进行模型训练，特别是在语料库增加了 更多样化的数据，就像这里建议的那样。我们希望表明，使用感知生成的声学模型可以 改进当前可用的开放访问语音识别系统(例如，Kaldi[13])的性能 新奇儿童言语刺激的识别。PERCEPT数据库将通过以下部分公开提供- 与美国国立卫生研究院资助的语音研究数据共享平台PhonBank合作(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]).