Publication

Abstract : This study introduces a novel technique for enhancing the performance of deep denoising autoencoders (DDAE) in speech processing for cochlear implants (CIs). For individuals with hearing loss, cochlear implants are electronic devices that help to restore their ability to hear. However, the performance of CIs speech intelligibility in the noisy environment is limited. One of the most commonly used methods for reducing noise in CIs is through a preprocessing technique called deep denoising autoencoder. DDAE models have shown potential in learning various noise patterns, but their performance in enhancing speech intelligibility is relatively low due to a ineffective objective function. To address this limitation, this study proposes a multi-objective technique to fine-tune the DDAE model. When multiple objectives are optimized simultaneously, the model becomes more robust and better at handling real-time noise. Based on the experimental findings, it has been confirmed that the proposed multi-objective learning technique performs better than other models when it comes to speech intelligibility. Furthermore, the enhanced signal is presented to the acoustic cochlear implant simulator to evaluate the improvement of speech intelligibility in CIs.