In the recent past years, deep learning based machine learning systems have demonstrated remarkable success for a wide range of learning tasks in multiple domains such as computer vision, speech… Click to show full abstract
In the recent past years, deep learning based machine learning systems have demonstrated remarkable success for a wide range of learning tasks in multiple domains such as computer vision, speech recognition and other pattern recognition based applications. The purpose of this article is to contribute a timely review and introduction of state-of-the-art deep learning techniques and their effectiveness in speech/acoustic signal processing. Thorough investigations of various deep learning architectures are provided under the categories of discriminative and generative algorithms, including the up-to-date Generative Adversarial Networks (GANs) as an integrated model. A comprehensive overview of applications in audio generation is highlighted. Based on understandings from these approaches, we discuss how deep learning methods can benefit the field of speech/acoustic signal synthesis and the potential issues that need to be addressed for prospective real-world scenarios. We hope this survey provides a valuable reference for practitioners seeking to innovate in the usage of deep learning approaches for speech/acoustic signal generation.
               
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