Exploring Acoustic Foundations in Speech Production Assessment Models for Children with Cochlear Implants

Abstract

Although substantial research has been conducted on automatic speech assessment models leveraging speech representations derived from self-supervised learning models, the underlying mechanisms remain relatively underexplored. This study investigates the acoustic foundations of automatic speech production skill assessment models for children with cochlear implants, which helps enhance model performance and elucidate the basis of assessment outcomes. We analyze the statistical differences in acoustic characteristics as a function of speech scores for articulation and prosody. Using a general probing approach, models are trained with layer-wise embeddings from wav2vec2.0 and probed through simple regression models. The probing model performance is interpreted as an indicator of the information encoded within the speech representations. Experimental results demonstrate that the assessment models capture distinct acoustic features depending on the target of assessment, shedding light on the acoustic basis of the results and revealing the strengths and limitations of the models.