Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis

Speech timing and monosyllabic diadochokinesis measures in the assessment of amyotrophic lateral sclerosis in Canadian French

PURPOSE

The primary objective of this study was to determine if speech and pause measures obtained using a passage reading task and timing measures from a monosyllabic diadochokinesis (DDK) task differ across speakers of Canadian French diagnosed with amyotrophic lateral sclerosis (ALS) presenting with and without bulbar symptoms, and healthy controls. The secondary objective was to determine if these measures can reflect the severity of bulbar symptoms.

METHOD

A total of 29 Canadian French speakers with ALS (classified as bulbar symptomatic [n = 14] or pre-symptomatic [n = 15]) and 17 age-matched healthy controls completed a passage reading task and a monosyllabic DDK task (/pa/ and /ta/), for up to three follow-up visits. Measures of speaking rate, total duration, speech duration, and pause events were extracted from the passage reading recordings using a semi-automated speech and pause analysis procedure. Manual analysis of DDK recordings provided measures of DDK rate and variability.

RESULT

Group comparisons revealed significant differences (p = < .05) between the symptomatic group and the pre-symptomatic and control groups for all passage measures and DDK rates. Only the DDK rate in /ta/ differentiated the pre-symptomatic and control groups. Repeated measures correlations revealed moderate correlations (rrm = > 0.40; p = < 0.05) between passage measures of total duration, speaking rate, speech duration, and number of pauses, and ALSFRS-R total and bulbar scores, as well as between DDK rate and ALSFRS-R total score.

CONCLUSION

Speech and pause measures in passage and timing measures in monosyllabic DDK tasks might be suitable for monitoring bulbar functional symptoms in French speakers with ALS, but more work is required to identify which measures are sensitive to the earliest stages of the disease.

Dysarthric Speech Transformer: A Sequence-to-Sequence Dysarthric Speech Recognition System

Automatic Speech Recognition (ASR) technologies can be life-changing for individuals who suffer from dysarthria, a speech impairment that affects articulatory muscles and results in incomprehensive speech. Nevertheless, the performance of the current dysarthric ASR systems is unsatisfactory, especially for speakers with severe dysarthria who most benefit from this technology. While transformer and neural attention-base sequences-to-sequence ASR systems achieved state-of-the-art results in converting healthy speech to text, their applications as a Dysarthric ASR remain unexplored due to the complexities of dysarthric speech and the lack of extensive training data. In this study, we addressed this gap and proposed our Dysarthric Speech Transformer that uses a customized deep transformer architecture. To deal with the data scarcity problem, we designed a two-phase transfer learning pipeline to leverage healthy speech, investigated neural freezing configurations, and utilized audio data augmentation. Overall, we trained 45 speaker-adaptive dysarthric ASR in our investigations. Results indicate the effectiveness of the transfer learning pipeline and data augmentation, and emphasize the significance of deeper transformer architectures. The proposed ASR outperformed the state-of-the-art and delivered better accuracies for 73% of the dysarthric subjects whose speech samples were employed in this study, in which up to 23% of improvements were achieved.

Validation of automated pipeline for the assessment of a motor speech disorder in amyotrophic lateral sclerosis (ALS)

Background and objective

Amyotrophic lateral sclerosis (ALS) frequently causes speech impairments, which can be valuable early indicators of decline. Automated acoustic assessment of speech in ALS is attractive, and there is a pressing need to validate such tools in line with best practices, including analytical and clinical validation. We hypothesized that data analysis using a novel speech assessment pipeline would correspond strongly to analyses performed using lab-standard practices and that acoustic features from the novel pipeline would correspond to clinical outcomes of interest in ALS.

Methods

We analyzed data from three standard speech assessment tasks (i.e., vowel phonation, passage reading, and diadochokinesis) in 122 ALS patients. Data were analyzed automatically using a pipeline developed by Winterlight Labs, which yielded 53 acoustic features. First, for analytical validation, data were analyzed using a lab-standard analysis pipeline for comparison. This was followed by univariate analysis (Spearman correlations between individual features in Winterlight and in-lab datasets) and multivariate analysis (sparse canonical correlation analysis (SCCA)). Subsequently, clinical validation was performed. This included univariate analysis (Spearman correlation between automated acoustic features and clinical measures) and multivariate analysis (interpretable autoencoder-based dimensionality reduction).

Results

Analytical validity was demonstrated by substantial univariate correlations (Spearman's ρ > 0.70) between corresponding pairs of features from automated and lab-based datasets, as well as interpretable SCCA feature groups. Clinical validity was supported by strong univariate correlations between automated features and clinical measures (Spearman's ρ > 0.70), as well as associations between multivariate outputs and clinical measures.

Conclusion

This novel, automated speech assessment feature set demonstrates substantial promise as a valid tool for analyzing impaired speech in ALS patients and for the further development of these technologies.