Speech Enhancement In Multiple-Noise Conditions using Deep Neural Networks
This addresses a practical problem for speech processing systems by moving beyond single-noise scenarios, though it is incremental as it builds on existing DNN methods.
The paper tackles speech enhancement in real-world conditions with multiple simultaneous noises, proposing DNN-based strategies and a psychoacoustic training approach, achieving improved speech quality in office environments.
In this paper we consider the problem of speech enhancement in real-world like conditions where multiple noises can simultaneously corrupt speech. Most of the current literature on speech enhancement focus primarily on presence of single noise in corrupted speech which is far from real-world environments. Specifically, we deal with improving speech quality in office environment where multiple stationary as well as non-stationary noises can be simultaneously present in speech. We propose several strategies based on Deep Neural Networks (DNN) for speech enhancement in these scenarios. We also investigate a DNN training strategy based on psychoacoustic models from speech coding for enhancement of noisy speech