Advanced Technology in Speech and Acoustic Signal Processing

Dear Colleagues,

We live in a world of sound, including the sound of nature, animals and humans. Uncovering information from sound waves remains an active research field, with challenges such as event detection, speech recognition, speaker recognition, and emotion recognition, to name a few. Conventional approaches are mostly based on statistical models, while the new generation of technologies extensively uses neural models as the backbone, partly due to the accumulation of large-scale data. In recent years, numerous novel methods have been proposed such as end-to-end modeling, effective data augmentation, large-scale pre-training, and smart design of architectures and loss functions. These new techniques have brought significant performance improvement, which in turn substantially extends the application scope of techniques. 

In spite of these tremendous successes, many challenges still exist. For instance, it is still hard to detect events with a limited appearance in the training data; performance of speech recognition for minor languages is still weak; and recognizing speakers from speech with interfering noise or speakers is still hard. Some of these challenges can be solved by prudent employment of existing methods, while others need new theories, algorithms and computational models. 

This special issue will focus on novel theories and methods as well as new experimental findings in various domains of speech and acoustic signal processing, including but not limited to speech enhancement, experimental acoustics, speech/speaker/emotion recognition, multimodal signal processing, and speech synthesis. 

Dr. Dong Wang
Dr. Andrew Abel
Guest Editors

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