Leveraging Self-Supervised Audio-Visual Pretrained Models to Improve Vocoded Speech Intelligibility in Cochlear Implant Simulation

Richard Lee Lai; Jen-Cheng Hou; I-Chun Chern; Kuo-Hsuan Hung; Yi-Ting Chen; Mandar Gogate; Tughrul Arslan; Amir Hussain; Yu Tsao

arXiv · 2307.07748

Leveraging Self-Supervised Audio-Visual Pretrained Models to Improve Vocoded Speech Intelligibility in Cochlear Implant Simulation

Richard Lee Lai·Jen-Cheng Hou·I-Chun Chern·Kuo-Hsuan Hung·Yi-Ting Chen·Mandar Gogate·Tughrul Arslan·Amir Hussain·Yu Tsao

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Abstract

Individuals with hearing impairments face challenges in their ability to comprehend speech, particularly in noisy environments. The aim of this study is to explore the effectiveness of audio-visual speech enhancement (AVSE) in enhancing the intelligibility of vocoded speech in cochlear implant (CI) simulations. Notably, the study focuses on a challenged scenario where there is limited availability of training data for the AVSE task. To address this problem, we propose a novel deep neural network framework termed Self-Supervised Learning-based AVSE (SSL-AVSE). The proposed SSL-AVSE combines visual cues, such as lip and mouth movements, from the target speakers with corresponding audio signals. The contextually combined audio and visual data are then fed into a Transformer-based SSL AV-HuBERT model to extract features, which are further processed using a BLSTM-based SE model. The results demonstrate several key findings. Firstly, SSL-AVSE successfully overcomes the issue of limited data by leveraging the AV-HuBERT model. Secondly, by fine-tuning the AV-HuBERT model parameters for the target SE task, significant performance improvements are achieved. Specifically, there is a notable enhancement in PESQ (Perceptual Evaluation of Speech Quality) from 1.43 to 1.67 and in STOI (Short-Time Objective Intelligibility) from 0.70 to 0.74. Furthermore, the performance of the SSL-AVSE was evaluated using CI vocoded speech to assess the intelligibility for CI users. Comparative experimental outcomes reveal that in the presence of dynamic noises encountered during human conversations, SSL-AVSE exhibits a substantial improvement. The NCM (Normal Correlation Matrix) values indicate an increase of 26.5% to 87.2% compared to the noisy baseline.

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BibTeX RIS

Richard Lee Lai, Jen-Cheng Hou, I-Chun Chern, Kuo-Hsuan Hung, Yi-Ting Chen, Mandar Gogate, Tughrul Arslan, Amir Hussain, Yu Tsao. 2025-10-04. Leveraging Self-Supervised Audio-Visual Pretrained Models to Improve Vocoded Speech Intelligibility in Cochlear Implant Simulation. https://arxiv.org/abs/2307.07748

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