AI-Driven Web-Based Speech Transcription Tool: A Novel Approach for Efficient Evaluation of Verbal Memory Performance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082289" target="_blank" >RIV/00159816:_____/25:00082289 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11284682" target="_blank" >https://ieeexplore.ieee.org/document/11284682</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JBHI.2025.3620128" target="_blank" >10.1109/JBHI.2025.3620128</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AI-Driven Web-Based Speech Transcription Tool: A Novel Approach for Efficient Evaluation of Verbal Memory Performance

Popis výsledku v původním jazyce

Memory deficits are prevalent in epilepsy and other brain disorders, significantly affecting quality of life. In particular, patients with mesial temporal lobe epilepsy require ongoing monitoring and repeated memory assessments to track cognitive function, which could benefit from automated transcription tools. We used a classic free recall (FR) verbal memory task, where participants recalled words, to assess their performance by recording, transcribing, and detecting vocalizations of correctly remembered words. Conventional manual speech transcription methods are time-consuming and prone to human error, especially in a noisy hospital environment. To address these limitations, a modified U-Net architecture was employed for noise reduction, resulting in a signal-to-noise ratio (SNR) of 15.8 and a mean squared error (MSE) of 0.0021. We also developed an automated transcription interface utilizing the Whisper speech recognition model, which was fine-tuned for Polish, Czech, and English languages. Dynamic Time Warping (DTW) was applied to provide precise word-level timestamps of vocalization onset and offset. The interface was iteratively refined over eight development cycles, incorporating feedback from target users. Transcription accuracy was evaluated with Word Error Rates (WER) of 10.3% for Czech, 7.1% for Polish, and 5% for English, alongside respective Character Error Rates (CER) of 12%, 10.8%, and 7.5%. Our automated interface outperformed manual transcription, reducing transcription time fourfold and achieving 87.5% user satisfaction. These results demonstrate robust transcription accuracy of the challenging Slavic languages and highlight the potential of automated transcription to streamline speech processing using emerging human-computer interface technologies.

Název v anglickém jazyce

AI-Driven Web-Based Speech Transcription Tool: A Novel Approach for Efficient Evaluation of Verbal Memory Performance

Popis výsledku anglicky

Memory deficits are prevalent in epilepsy and other brain disorders, significantly affecting quality of life. In particular, patients with mesial temporal lobe epilepsy require ongoing monitoring and repeated memory assessments to track cognitive function, which could benefit from automated transcription tools. We used a classic free recall (FR) verbal memory task, where participants recalled words, to assess their performance by recording, transcribing, and detecting vocalizations of correctly remembered words. Conventional manual speech transcription methods are time-consuming and prone to human error, especially in a noisy hospital environment. To address these limitations, a modified U-Net architecture was employed for noise reduction, resulting in a signal-to-noise ratio (SNR) of 15.8 and a mean squared error (MSE) of 0.0021. We also developed an automated transcription interface utilizing the Whisper speech recognition model, which was fine-tuned for Polish, Czech, and English languages. Dynamic Time Warping (DTW) was applied to provide precise word-level timestamps of vocalization onset and offset. The interface was iteratively refined over eight development cycles, incorporating feedback from target users. Transcription accuracy was evaluated with Word Error Rates (WER) of 10.3% for Czech, 7.1% for Polish, and 5% for English, alongside respective Character Error Rates (CER) of 12%, 10.8%, and 7.5%. Our automated interface outperformed manual transcription, reducing transcription time fourfold and achieving 87.5% user satisfaction. These results demonstrate robust transcription accuracy of the challenging Slavic languages and highlight the potential of automated transcription to streamline speech processing using emerging human-computer interface technologies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

<a href="/cs/project/GF22-28594K" target="_blank" >GF22-28594K: Pokročilé algoritmy pro identifikaci elektrofyziologických příznaků kódování a vybavování lidské paměti v intrakraniálním EEG</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Journal of Biomedical and Health Informatics

ISSN

2168-2194

e-ISSN

2168-2208

Svazek periodika

29

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

8703-8710

Kód UT WoS článku

001640358300014

EID výsledku v databázi Scopus

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