Noise-Robust Voice Conversion

A persistent challenge in speech processing is the presence of noise that reduces the quality of speech signals. Whether natural speech is used as input or speech is the desirable output to be synthesized, noise degrades the performance of these systems and causes output speech to be unnatural. Speech enhancement deals with such a problem, typically seeking to improve the input speech or post-processes the (re)synthesized speech. An intriguing complement to post-processing speech signals is voice conversion, in which speech by one person (source speaker) is made to sound as if spoken by a different person (target speaker). Traditionally, the majority of speech enhancement and voice conversion methods rely on parametric modeling of speech. A promising complement to parametric models is an inventory-based approach, which is the focus of this work. In inventory-based speech systems, one records an inventory of clean speech signals as a reference. Noisy speech (in the case of enhancement) or target speech (in the case of conversion) can then be replaced by the best-matching clean speech in the inventory, which is found via a correlation search method. Such an approach has the potential to alleviate intelligibility and unnaturalness issues often encountered by parametric modeling speech processing systems. This work investigates and compares inventory-based speech enhancement methods with conventional ones. In addition, the inventory search method is applied to estimate source speaker characteristics for voice conversion in noisy environments. Two noisy-environment voice conversion systems were constructed for a comparative study: a direct voice conversion system and an inventory-based voice conversion system, both with limited noise filtering at the front end. Results from this work suggest that the inventory method offers encouraging improvements over the direct conversion method

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao\u27s method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims