Liss, Julie M

Purpose: This study aimed to evaluate how speech signal compression algorithms affect the acoustic and perceptual characteristics of dysarthric speech. As telepractice becomes more common in speech-language pathology, particularly in the assessment of dysarthria, understanding the impact of telecommunication compression on speech fidelity is essential for clinical decision making. Method: Speech samples were collected from 15 individuals with dysarthria, recorded locally and simultaneously via Zoom (Version 6.2.11). The Opus codec was used to process the locally recorded speech samples under fullband, wideband, and narrowband compression conditions. Acoustic measures were derived for all samples. Twenty experienced speech-language pathologists (SLPs) rated vocal quality and nasality for sustained vowels and rated articulatory precision and orthographically transcribed sentences across each condition. Results: Narrowband compression was associated with significantly degraded voice quality measures (e.g., harmonic-to-noise ratio, shimmer) and reduced transcription accuracy. Despite these changes in acoustics and intelligibility, ratings of articulatory precision, nasality, and vocal quality remained stable across conditions, suggesting SLPs could adjust for compression artifacts when making perceptual judgments. Conclusions: Speech compression, particularly in low-bandwidth (i.e., narrow) condition, impacts acoustic fidelity and intelligibility, though expert listeners maintain reliable perceptual judgments. These findings underscore the need to consider compression effects in telepractice and highlight the importance of developing optimized protocols for remote dysarthria assessment.

Purpose: Model interpretability is a critical requirement for deploying artificial intelligence (AI) applications in clinical speech settings, yet existing evaluation methods often overlook this aspect. The complex nature of speech features and the opaque decision-making processes of AI models underscore the need for a tailored framework to assess interpretability in speech-based clinical AI systems. This article proposes an initial framework composed of eight key factors to consider when evaluating the interpretability of clinical speech AI models. These factors are categorized into two groups: functional factors and clinician-centered factors. The functional factors, which can be assessed independently without user involvement, include faithfulness and computational efficiency. The clinician-centered factors include four established ones from existing literature (cognitive load, human–AI task performance, mental model, and user trust) and two new factors tailored to the unique demands of clinical speech applications: clinical understandability and decision relevance. We further suggest evaluation methods for each of the identified factors and propose modifying existing instruments such as the System Usability Scale and the Healthcare Systems Usability Scale to evaluate the two newly introduced factors. Conclusions: Our identified factors form an initial framework for evaluating the interpretability of speech-based clinical AI systems, supporting the effective integration of AI into clinical workflows. Future works include conducting an evaluation experiment using the proposed framework and refining it further based on the findings from the experiment.