12 accepted papers
Self-supervised learning (SSL) has grown in interest within the speech processing community, since it produces representations that are useful for many downstream tasks. SSL uses global and contextual methods to produce robust representations, where SSL even outperforms supervised models. Many self-…
Objective speech quality measures are typically used to assess speech enhancement algorithms, but it has been shown that they are sub-optimal as learning objectives because they do not always align well with human subjective ratings. This misalignment often results in noticeable distortions and arti…
Speech assessment is crucial for many applications, but current intrusive methods cannot be used in real environments. Data-driven approaches have been proposed, but they use simulated speech materials or only estimate objective scores. In this paper, we propose a novel multi-task non-intrusive appr…
Reverberation time, T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</inf> , directly influences the amount of reverberation in a signal, and its direct estimation may help with dereverberation. Traditionally, T <inf xmlns:mml="http://www.w3.org/199…
Recent work has shown that deep-learning based speech enhancement performs best when a time-frequency mask is estimated. Unlike speech, these masks have a small range of values that better facilitate regression-based learning. The question remains whether neural-network based speech estimation shoul…
Dereverberation is often performed in the time-frequency domain using mostly deep learning approaches. Time-frequency domain processing, however, may not be necessary when reverberation is modeled by the convolution operation. In this paper, we investigate whether deverberation can be effectively pe…
Computational objective metrics that use reference signals have been shown to be effective forms of speech assessment in simulated environments, since they are correlated with subjective listening studies. Recent efforts have been dedicated towards effective forms of reference-less assessment to mak…
Speech enhancement has greatly benefited from deep learning. Currently, the best performing deep architectures use long short-term memory (LSTM) recurrent neural networks (RNNs) to model short and long temporal dependencies. These approaches, however, underutilize or ignore spectral-level dependenci…
Many speech enhancement algorithms have been proposed over the years and it has been shown that deep neural networks can lead to significant improvements. These algorithms, however, have not been validated for hearing-impaired listeners. Additionally, these algorithms are often evaluated under a lim…
Traditional speech separation systems enhance the magnitude response of noisy speech. Recent studies, however, have shown that perceptual speech quality is significantly improved when magnitude and phase are both enhanced. These studies, however, have not determined if phase enhancement is beneficia…
The phase response of noisy speech has largely been ignored, but recent research shows the importance of phase for perceptual speech quality. A few phase enhancement approaches have been developed. These systems, however, require a separate algorithm for enhancing the magnitude response. In this pap…
This paper presents an approach for improving the perceptual quality of speech separated from background noise at low signal-to-noise ratios. Our approach uses two stages of deep neural networks, where the first stage estimates the ideal ratio mask that separates speech from noise, and the second st…