Diagnosis of pathological speech with streamlined features for long short-term memory learning

Tuan D Pham; Simon B Holmes; Lifong Zou; Mangala Patel; Paul Coulthard

doi:10.1016/j.compbiomed.2024.107976

Diagnosis of pathological speech with streamlined features for long short-term memory learning

Comput Biol Med. 2024 Mar:170:107976. doi: 10.1016/j.compbiomed.2024.107976. Epub 2024 Jan 8.

Authors

Tuan D Pham¹, Simon B Holmes², Lifong Zou², Mangala Patel², Paul Coulthard²

Affiliations

¹ Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, Turner Street, E1 2AD, London, UK. Electronic address: tuan.pham@qmul.ac.uk.
² Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, Turner Street, E1 2AD, London, UK.

PMID: 38219647
DOI: 10.1016/j.compbiomed.2024.107976

Abstract

Background: Pathological speech diagnosis is crucial for identifying and treating various speech disorders. Accurate diagnosis aids in developing targeted intervention strategies, improving patients' communication abilities, and enhancing their overall quality of life. With the rising incidence of speech-related conditions globally, including oral health, the need for efficient and reliable diagnostic tools has become paramount, emphasizing the significance of advanced research in this field.

Methods: This paper introduces novel features for deep learning in the analysis of short voice signals. It proposes the incorporation of time-space and time-frequency features to accurately discern between two distinct groups: Individuals exhibiting normal vocal patterns and those manifesting pathological voice conditions. These advancements aim to enhance the precision and reliability of diagnostic procedures, paving the way for more targeted treatment approaches.

Results: Utilizing a publicly available voice database, this study carried out training and validation using long short-term memory (LSTM) networks learning on the combined features, along with a data balancing strategy. The proposed approach yielded promising performance metrics: 90% accuracy, 93% sensitivity, 87% specificity, 88% precision, an F₁ score of 0.90, and an area under the receiver operating characteristic curve of 0.96. The results surpassed those obtained by the networks trained using wavelet-time scattering coefficients, as well as several algorithms trained with alternative feature types.

Conclusions: The incorporation of time-frequency and time-space features extracted from short segments of voice signals for LSTM learning demonstrates significant promise as an AI tool for the diagnosis of speech pathology. The proposed approach has the potential to enhance the accuracy and allow for real-time pathological speech assessment, thereby facilitating more targeted and effective therapeutic interventions.

Keywords: Artificial intelligence; Deep learning; Diagnosis; Feature extraction; Pathological voice.

MeSH terms

Humans
Memory, Short-Term
Quality of Life
Reproducibility of Results
Speech Disorders
Speech*
Speech-Language Pathology*