![]() Still, the evidence for clinical validity of ASR-as applied to a specific clinical population-remains scant and has primarily been evaluated for aphasia and AOS (e.g., Ballard et al., 2019 Jacks et al., 2019). 69) for speakers with dysarthria when using the Google ASR engine. (2016) found a moderate correlation (Pearson r =. Looking at the relationship between Google ASR accuracy and clinician-rated severity, Tu et al. (2019) found agreement of 75.7% between human and ASR (using CMU PocketSphinx) judgments of word-level productions by people with aphasia and AOS following stroke. (2010) reported Spearman rho between −.88 and −.90 for a hidden Markov model (HMM)-based ASR system used on head and neck cancer patients with dysglossia and dysphonia and Ballard et al. 96–.98) using IBM Watson for speakers with aphasia and/or apraxia of speech (AOS) following a stroke Maier et al. ![]() (2019) found very high correlations (Spearman ρ =. For example, comparing human transcription and ASR transcription, Jacks et al. Other groups have made inroads in clinically validating ASR for dysarthria by investigating the relationship between perceptual severity measures and ASR transcription ( Tu et al., 2016). Clinical validation is defined as whether a measure “acceptably identifies, measures, or predicts a meaningful clinical, physical, functional state, or experience, in the stated context of use” ( Goldsack et al., 2020). In the current project, we tested the clinical validity of a widely available OTS ASR system (Google Cloud ASR) for grading speech severity in persons with ALS ( Goldsack et al., 2020 Google LLC, 2020). These limitations suggest that ASR may be unpredictable when indexing severity and problematic for judging clinically relevant speech differences. Moreover, reports that humans and ASR systems produce different errors when transcribing speech limit the reliability of ASR for tracking functional speech changes ( Mulholland et al., 2016). There is also evidence that some speech deviations affect ASR accuracy more than others ( Benzeghiba et al., 2007 Goldwater et al., 2010 Keshet, 2018 Tu et al., 2016), such that speakers with certain dysarthria etiologies or subsystem impairments could be erroneously classified as more or less severe regardless of actual dysarthria severity. The word-level transcription of most OTS ASR systems could further obfuscate subtle differences at the phone level, as a mild distortion and a major articulatory deviation could lead to the selection of the same incorrect word ( Keshet, 2018). For example, language models can boost accuracy by aiding word prediction, or they can decrease accuracy when errors lower the probability of correctly selecting nearby words ( Keshet, 2018). Moreover, previous work has identified several threats to ASR validity, including biases from language models. 80).ĭespite these advantages, the efficacy of ASR has been understudied for speech severity grading. (2016) found a strong correlation between ASR WER and perceptually rated severity WER (Pearson r =. Prior research in people both with and without speech impairment has linked ASR accuracy to speech intelligibility ( Ferrier et al., 1995 Jacks et al., 2019 McHenry & Laconte, 2010 Riedhammer et al., 2007), with correlations reported between. Because OTS ASR platforms are trained on typical speech, recognition accuracy degrades as speech becomes more atypical or less intelligible ( De Russis & Corno, 2019 Mustafa et al., 2015). The proportion of words incorrectly recognized by these systems, or word error rate (WER), could presumably serve as a quantitative index of overall speech impairment. Off-the-shelf automatic speech recognition (OTS ASR) systems are an attractive candidate for this application, because they are low cost, simple to implement, and widely available. The need for more objective and automatic methods for assessing speech severity in motor speech disorders is widely recognized for a variety of research and clinical applications, including improved diagnosis, symptom monitoring, and intervention design ( King et al., 2012 Tjaden & Liss, 1995). While found to be reliable ( Stipancic et al., 2021), this assessment requires experienced listeners, can be time intensive and costly, and can be biased by the assessor's familiarity with the speaker, speech disorder, and subject matter ( King et al., 2012 Tjaden & Liss, 1995). The current standard for assessing speech impairment severity requires licensed speech-language pathologists (SLPs), who use ordinal descriptors (e.g., mild, moderate, and severe King et al., 2012 Tjaden & Liss, 1995). The demand for automated speech analysis systems is increasing due to their potential value as biomarkers for a variety of mental and physical health conditions ( Low et al., 2020 Toth et al., 2018).
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