Automatic Detection of Speech Sound Disorder in Cantonese-Speaking Pre-School Children

Abstract

Speech sound disorder (SSD) is a type of developmental disorder in which children encounter persistent difficulties in correctly producing certain speech sounds. Conventionally, assessment of SSD relies largely on speech and language pathologists (SLPs) with appropriate language background. With the unsatisfied demand for qualified SLPs, automatic detection of SSD is highly desirable for assisting clinical work and improving the efficiency and quality of services. In this paper, methods and systems for fully automatic detection of SSD in young children are investigated. A microscopic approach and a macroscopic approach are developed. The microscopic system is based on detection of phonological errors in impaired child speech. A deep neural network (DNN) model is trained to learn the similarity and contrast between consonant segments. Phonological error is identified by contrasting a test speech segment to reference segments. The phone-level similarity scores are aggregated for speaker-level SSD detection. The macroscopic approach leverages holistic changes of speech characteristics related to disorders. Various types of speaker-level embeddings are investigated and compared. Experimental results show that the proposed microscopic system achieves unweighted average recall (UAR) from 84.0% to 91.9% on phone-level error detection. The proposed macroscopic approach can achieve a UAR of 89.0% on speaker-level SSD detection. The speaker embeddings adopted for macroscopic SSD detection can effectively discard the information related to speaker's personal identity.