Mizhou Hui

John Alderete, Macarious Kin Fung Hui, Aanchan Mohan

The Simon Fraser University Speech Error Database (SFUSED) is a public data collection developed for linguistic and psycholinguistic research. Here we demonstrate how its design and annotations can be used to test and evaluate speech recognition models. The database comprises systematically annotated speech errors from spontaneous English speech, with each error tagged for intended and actual error productions. The annotation schema incorporates multiple classificatory dimensions that are of some value to model assessment, including linguistic hierarchical level, contextual sensitivity, degraded words, word corrections, and both word-level and syllable-level error positioning. To assess the value of these classificatory variables, we evaluated the transcription accuracy of WhisperX across 5,300 documented word and phonological errors. This analysis demonstrates the atabase's effectiveness as a diagnostic tool for ASR system performance.

Macarious Hui, Jinda Zhang, Aanchan Mohan

Individuals with cerebral palsy (CP) and amyotrophic lateral sclerosis (ALS) frequently face challenges with articulation, leading to dysarthria and resulting in atypical speech patterns. In healthcare settings, communication breakdowns reduce the quality of care. While building an augmentative and alternative communication (AAC) tool to enable fluid communication we found that state-of-the-art (SOTA) automatic speech recognition (ASR) technology like Whisper and Wav2vec2.0 marginalizes atypical speakers largely due to the lack of training data. Our work looks to leverage SOTA ASR followed by domain specific error-correction. English dysarthric ASR performance is often evaluated on the TORGO dataset. Prompt-overlap is a well-known issue with this dataset where phrases overlap between training and test speakers. Our work proposes an algorithm to break this prompt-overlap. After reducing prompt-overlap, results with SOTA ASR models produce extremely high word error rates for speakers with mild and severe dysarthria. Furthermore, to improve ASR, our work looks at the impact of n-gram language models and large-language model (LLM) based multi-modal generative error-correction algorithms like Whispering-LLaMA for a second pass ASR. Our work highlights how much more needs to be done to improve ASR for atypical speakers to enable equitable healthcare access both in-person and in e-health settings.