Version 1
: Received: 27 February 2024 / Approved: 27 February 2024 / Online: 27 February 2024 (16:45:37 CET)
How to cite:
Jelassi, M.; Matteli, K.; Ben Khalfallah, H.; Demongeot, J. Enhancing Mental Health Support through Artificial Intelligence: Advances in Speech and Text Analysis within Online Therapy Platforms. Preprints2024, 2024021585. https://doi.org/10.20944/preprints202402.1585.v1
Jelassi, M.; Matteli, K.; Ben Khalfallah, H.; Demongeot, J. Enhancing Mental Health Support through Artificial Intelligence: Advances in Speech and Text Analysis within Online Therapy Platforms. Preprints 2024, 2024021585. https://doi.org/10.20944/preprints202402.1585.v1
Jelassi, M.; Matteli, K.; Ben Khalfallah, H.; Demongeot, J. Enhancing Mental Health Support through Artificial Intelligence: Advances in Speech and Text Analysis within Online Therapy Platforms. Preprints2024, 2024021585. https://doi.org/10.20944/preprints202402.1585.v1
APA Style
Jelassi, M., Matteli, K., Ben Khalfallah, H., & Demongeot, J. (2024). Enhancing Mental Health Support through Artificial Intelligence: Advances in Speech and Text Analysis within Online Therapy Platforms. Preprints. https://doi.org/10.20944/preprints202402.1585.v1
Chicago/Turabian Style
Jelassi, M., Houssem Ben Khalfallah and Jacques Demongeot. 2024 "Enhancing Mental Health Support through Artificial Intelligence: Advances in Speech and Text Analysis within Online Therapy Platforms" Preprints. https://doi.org/10.20944/preprints202402.1585.v1
Abstract
In the dynamic field of mental health care, the nuanced application of Artificial Intelligence (AI) through Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) is a pivotal innovation in patient empowerment and service optimization. This study introduces a distinctive online therapy platform that capitalizes on the synergy of NLP and ASR to offer unprecedented levels of interactive and personalized therapeutic interventions. The architecture of our system is meticulously detailed, featuring an ASR component with an impressive Word Error Rate (WER) of 14% when trained on the diverse French subsets of the Mozilla Common Voice dataset, complemented by a high-precision NLP framework skilled in processing and responding to user inputs. The evaluation of our system highlights its efficacy in enhancing therapy sessions and user satisfaction, with an emphasis on the qualitative aspects of user feedback. The paper addresses challenges such as dataset representativeness and language model refinement and articulates the strategic solutions employed to overcome them. The paper concludes with forward-looking perspectives on AI's role in mental health services, advocating for the creation of sophisticated, language-specific datasets and models to satisfy the increasing demands of online therapy, reflecting a growing commitment of patients in the management of their therapy. This research underscores the transformative impact of AI in advancing mental health care into the digital age, representing a significant evolution over existing methodologies.
Keywords
Conversational AI; Automatic Speech Recognition (ASR); Natural Language Processing (NLP); Online Therapy Platforms; AI in Mental Healthcare
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
