Citation

Ain, Qurat Ul and Afzal, Hammad and Subhan, Fazli and Mohd Su'ud, Mazliham and Jung, Younhyun (2026) Advancing Dysarthric Speech-to-Text Recognition with LATTE: A Low-Latency Acoustic Modeling Approach for Real-Time Communication. Big Data. ISSN 2167-6461

Text Advancing Dysarthric Speech-to-Text Recognition with LATTE_ A Low-Latency Acoustic Modeling Approach for Real-Time Communication.pdf - Published Version Restricted to Repository staff only Download (845kB)

Abstract

Dysarthria, a motor speech disorder characterized by slurred and often unintelligible speech, presents substantial challenges for effective communication. Conventional automatic speech recognition systems frequently underperform on dysarthric speech, particularly in severe cases. To address this gap, we introduce low-latency acoustic transcription and textual encoding (LATTE), an advanced framework designed for real-time dysarthric speech recognition. LATTE integrates preprocessing, acoustic processing, and transcription mapping into a unified pipeline, with its core powered by a hybrid architecture that combines convolutional layers for acoustic feature extraction with bidirectional temporal layers for modeling temporal dependencies. Evaluated on the UA-Speech dataset, LATTE achieves a word error rate of 12.5%, phoneme error rate of 8.3%, and a character error rate of 1%. By enabling accurate, low-latency transcription of impaired speech, LATTE provides a robust foundation for enhancing communication and accessibility in both digital applications and real-time interactive environments.

Item Type:	Article
Uncontrolled Keywords:	Speech recognition systems
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Mar 2026 01:47
Last Modified:	02 Mar 2026 01:47
URII:	http://shdl.mmu.edu.my/id/eprint/15396

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