IEEE / ACM音频,语音和语言处理TASLP(IEEE Transactions on Audio, Speech, and Language Processing)期刊致力于处理代表音频,语音和语言的信号及其应用的创新理论和方法。这包括此类信号的分析,合成,增强,转换,分类和解释,以及相关信号处理系统的设计,开发和评估。 官网地址:


In this article, we investigate whispered-to natural-speech conversion method using sequence to sequence generation approach by proposing modified transformer architecture. We investigate different kinds of features such as mel frequency cepstral coefficients (MFCCs) and smoothed spectral features. The network is trained end-to-end (E2E) using supervised approach. We investigate the effectiveness of embedded auxillary decoder used after N encoder sub-layers, and is trained with the frame level objective function for identifying source phoneme labels. We predict target audio features and generate audio using these for testing. We test on standard wTIMIT dataset and CHAINS dataset. We report results as word-error-rate (WER) generated by using automatic speech recognition (ASR) system and also BLEU scores. %intelligibility and naturalness using mean opinion score and additionally using word error rate using automatic speech recognition system. In addition, we measure spectral shape of an output speech signal by measuring formant distributions w.r.t the reference speech signal, at frame level. In relation to this aspect, we also found that the whispered-to-natural converted speech formants probability distribution is closer to ground truth distribution. To the authors' best knowledge, this is the first time transformer with auxiliary decoder has been applied for whispered-to-natural speech conversion. [This pdf is TASLP submission draft version 1.0, 14th April 2020.]