Automatic Detection of Phone-Based Anomalies in Dysarthric Speech

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Purpose

Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.

Methods

The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.

Originality

This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.

Findings

The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

Construction of an automatic score for the evaluation of speech disorders among patients treated for a cancer of the oral cavity or the oropharynx: The Carcinologic Speech Severity Index

BACKGROUND

Speech disorders impact quality of life for patients treated with oral cavity and oropharynx cancers. However, there is a lack of uniform and applicable methods for measuring the impact on speech production after treatment in this tumor location.

OBJECTIVE

The objective of this work is to (1) model an automatic severity index of speech applicable in clinical practice, that is equivalent or superior to a severity score obtained by human listeners, via several acoustics parameters extracted (a) directly from speech signal and (b) resulting from speech processing and (2) derive an automatic speech intelligibility classification (i.e., mild, moderate, severe) to predict speech disability and handicap by combining the listener comprehension score with self-reported quality of life related to speech.

METHODS

Eighty-seven patients treated for cancer of the oral cavity or the oropharynx and 35 controls performed different tasks of speech production and completed questionnaires on speech-related quality of life. The audio recordings were then evaluated by human perception and automatic speech processing. Then, a score was developed through a classic logistic regression model allowing description of the severity of patients' speech disorders.

RESULTS

Among the group of parameters subject to extraction from automatic processing of the speech signal, six were retained, producing a correlation at 0.87 with the perceptual reference score, 0.77 with the comprehension score, and 0.5 with speech-related quality of life. The parameters that contributed the most are based on automatic speech recognition systems. These are mainly the automatic average normalized likelihood score on a text reading task and the score of cumulative rankings on pseudowords. The reduced automatic YC2SI is modeled in this way: Y_C2SIp  = 11.48726 + (1.52926 × _{Xaveraged normalized likelihood reading} ) + (-1.94e-06 × _{Xscore of cumulative ranks pseudowords} ).

CONCLUSION

Automatic processing of speech makes it possible to arrive at valid, reliable, and reproducible parameters able to serve as references in the framework of follow-up of patients treated for cancer of the oral cavity or the oropharynx.