Chiang-Jen Peng

Yun-Ju Chan, Chiang-Jen Peng, Syu-Siang Wang et al.

Numerous voice conversion (VC) techniques have been proposed for the conversion of voices among different speakers. Although good quality of the converted speech can be observed when VC is applied in a clean environment, the quality degrades drastically when the system is run in noisy conditions. In order to address this issue, we propose a novel speech enhancement (SE)-assisted VC system that utilizes the SE techniques for signal pre-processing, where the VC and SE components are optimized in an joint training strategy with the aim to provide high-quality converted speech signals. We adopt a popular model, StarGAN, as the VC component and thus call the combined system as EStarGAN. We test the proposed EStarGAN system using a Mandarin speech corpus. The experimental results first verified the effectiveness of joint training strategy used in EStarGAN. Moreover, EStarGAN demonstrated performance robustness in various unseen noisy environments. The subjective listening test results further showed that EStarGAN can improve the sound quality of speech signals converted from noise-corrupted source utterances.

Chiang-Jen Peng, Yun-Ju Chan, Cheng Yu et al.

Multi-task learning (MTL) and attention mechanism have been proven to effectively extract robust acoustic features for various speech-related tasks in noisy environments. In this study, we propose an attention-based MTL (ATM) approach that integrates MTL and the attention-weighting mechanism to simultaneously realize a multi-model learning structure that performs speech enhancement (SE) and speaker identification (SI). The proposed ATM system consists of three parts: SE, SI, and attention-Net (AttNet). The SE part is composed of a long-short-term memory (LSTM) model, and a deep neural network (DNN) model is used to develop the SI and AttNet parts. The overall ATM system first extracts the representative features and then enhances the speech signals in LSTM-SE and specifies speaker identity in DNN-SI. The AttNet computes weights based on DNN-SI to prepare better representative features for LSTM-SE. We tested the proposed ATM system on Taiwan Mandarin hearing in noise test sentences. The evaluation results confirmed that the proposed system can effectively enhance speech quality and intelligibility of a given noisy input. Moreover, the accuracy of the SI can also be notably improved by using the proposed ATM system.