For a given mixture of speech and noise, an ideal binary time-frequency mask is constructed by comparing speech energy and noise energy within local time-frequency units. It is observed that listeners achieve nearly perfect speech recognition from gated noise with binary gains prescribed by the ideal binary mask. Only 16 filter channels and a frame rate of 100 Hz are sufficient for high intelli...

Monaural speech segregation is an important problem in robust speech processing and has been formulated as a supervised learning problem. In supervised learning methods, the ideal binary mask (IBM) is usually used as the target because of its simplicity and large speech intelligibility gains. Recently, the ideal ratio mask (IRM) has been found to improve the speech quality over the IBM. However...

This article presents two studies investigating how the situation in which speech is uttered affects the frequency with which words are reduced. Study 1 is based on the Spoken Dutch Corpus, which consists of 15 components, nearly all representing a different speech situation. This study shows that the components differ in how often ten semantically weak words are highly reduced. The differences...

This article presents two studies investigating how the situation in which speech is uttered affects the frequency with which words are reduced. Study 1 is based on the Spoken Dutch Corpus, which consists of 15 components, nearly all representing a different speech situation. This study shows that the components differ in how often ten semantically weak words are highly reduced. The differences...

Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN) based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences be...

Humans modify their voice in interfering noise in order to maintain the intelligibility of their speech – this is called the Lombard effect. This ability, however, has not been extensively modeled in speech synthesis. Here we compare several methods of synthesizing speech in noise using a physiologically based statistical speech synthesis system (GlottHMM). The results show that in a realistic ...