Apraxia World: Deploying a Mobile Game and Automatic Speech Recognition for Independent Child Speech Therapy

Children with speech sound disorders typically improve pronunciation quality by undergoing speech therapy, which must be delivered frequently and with high intensity to be effective. As such, clinic sessions are supplemented with home practice, often under caregiver supervision. However, traditional home practice can grow boring for children due to monotony. Furthermore, practice frequency is limited by caregiver availability, making it difficult for some children to reach therapy dosage. To address these issues, this dissertation presents a novel speech therapy game to increase engagement, and explores automatic pronunciation evaluation techniques to afford children independent practice. Children with speech sound disorders typically improve pronunciation quality by undergoing speech therapy, which must be delivered frequently and with high intensity to be effective. As such, clinic sessions are supplemented with home practice, often under caregiver supervision. However, traditional home practice can grow boring for children due to monotony. Furthermore, practice frequency is limited by caregiver availability, making it difficult for some children to reach therapy dosage. To address these issues, this dissertation presents a novel speech therapy game to increase engagement, and explores automatic pronunciation evaluation techniques to afford children independent practice. The therapy game, called Apraxia World, delivers customizable, repetition-based speech therapy while children play through platformer-style levels using typical on-screen tablet controls; children complete in-game speech exercises to collect assets required to progress through the levels. Additionally, Apraxia World provides pronunciation feedback according to an automated pronunciation evaluation system running locally on the tablet. Apraxia World offers two advantages over current commercial and research speech therapy games; first, the game provides extended gameplay to support long therapy treatments; second, it affords some therapy practice independence via automatic pronunciation evaluation, allowing caregivers to lightly supervise instead of directly administer the practice. Pilot testing indicated that children enjoyed the game-based therapy much more than traditional practice and that the exercises did not interfere with gameplay. During a longitudinal study, children made clinically-significant pronunciation improvements while playing Apraxia World at home. Furthermore, children remained engaged in the game-based therapy over the two-month testing period and some even wanted to continue playing post-study. The second part of the dissertation explores word- and phoneme-level pronunciation verification for child speech therapy applications. Word-level pronunciation verification is accomplished using a child-specific template-matching framework, where an utterance is compared against correctly and incorrectly pronounced examples of the word. This framework identified mispronounced words better than both a standard automated baseline and co-located caregivers. Phoneme-level mispronunciation detection is investigated using a technique from the second-language learning literature: training phoneme-specific classifiers with phonetic posterior features. This method also outperformed the standard baseline, but more significantly, identified mispronunciations better than student clinicians

Automatic Screening of Childhood Speech Sound Disorders and Detection of Associated Pronunciation Errors

Speech disorders in children can affect their fluency and intelligibility. Delay in their diagnosis and treatment increases the risk of social impairment and learning disabilities. With the significant shortage of Speech and Language Pathologists (SLPs), there is an increasing interest in Computer-Aided Speech Therapy tools with automatic detection and diagnosis capability. However, the scarcity and unreliable annotation of disordered child speech corpora along with the high acoustic variations in the child speech data has impeded the development of reliable automatic detection and diagnosis of childhood speech sound disorders. Therefore, this thesis investigates two types of detection systems that can be achieved with minimum dependency on annotated mispronounced speech data. First, a novel approach that adopts paralinguistic features which represent the prosodic, spectral, and voice quality characteristics of the speech was proposed to perform segment- and subject-level classification of Typically Developing (TD) and Speech Sound Disordered (SSD) child speech using a binary Support Vector Machine (SVM) classifier. As paralinguistic features are both language- and content-independent, they can be extracted from an unannotated speech signal. Second, a novel Mispronunciation Detection and Diagnosis (MDD) approach was introduced to detect the pronunciation errors made due to SSDs and provide low-level diagnostic information that can be used in constructing formative feedback and a detailed diagnostic report. Unlike existing MDD methods where detection and diagnosis are performed at the phoneme level, the proposed method achieved MDD at the speech attribute level, namely the manners and places of articulations. The speech attribute features describe the involved articulators and their interactions when making a speech sound allowing a low-level description of the pronunciation error to be provided. Two novel methods to model speech attributes are further proposed in this thesis, a frame-based (phoneme-alignment) method leveraging the Multi-Task Learning (MTL) criterion and training a separate model for each attribute, and an alignment-free jointly-learnt method based on the Connectionist Temporal Classification (CTC) sequence to sequence criterion. The proposed techniques have been evaluated using standard and publicly accessible adult and child speech corpora, while the MDD method has been validated using L2 speech corpora