In this paper an estimator for speech enhancement based on Laplacian Mixture Model has been proposed. The proposed method, estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE  estimator, when the clean speech DFT coefficients are supposed mixture of Laplacians and the DFT coefficients of  noise are assumed zero-mean Gaussian distribution. Furthermore, the MMS...

In speech enhancement, soft decision, in which the speech absence probability (SAP) is introduced to modify the spectral gain or update the noise power, is known to be efficient. In many previous works, a fixed a priori probability of speech absence (q) is assumed in estimating the SAP, which is not realistic since speech is quasi-stationary and may not be present in each frequency bin. To addr...

In this paper, we propose a speech-presence uncertainty estimation to improve the global soft decision-based speech enhancement technique by using the spectral gradient scheme. The conventional soft decision-based speech enhancement technique uses a fixed ratio (Q) of the a priori speech-presence and speech-absence probabilities to derive the speech-absence probability (SAP). However, we attemp...

We investigate whether four metacognitive metrics derived from student correctness and uncertainty values are predictive of student learning in a fully automated spoken dialogue computer tutoring corpus. We previously showed that these metrics predicted learning in a comparable wizarded corpus, where a human wizard performed the speech recognition and correctness and uncertainty annotation. Our...

Speech enhancement algorithms which are based on estimating the short-time spectral amplitude of the clean speech have better performance when a soft-decision gain modification, depending on the a priori probability of speech absence, is used. In reported works a fixed probability, q, is assumed. Since speech is non-stationary and may not be present in every frequency bin when voiced, we propos...

In this paper, we investigate the use of the minimum mean square error (MMSE) spectral energy estimator for use in environmentrobust automatic speech recognition (ASR). In the past, it has been common to use the MMSE log-spectral amplitude estimator for this task. However, this estimator was originally derived under subjective human listening criteria. Therefore its complex suppression rule may...

In this paper we investigate the enhancement of speech by applying MMSE short-time spectral magnitude estimation in the modulation domain. For this purpose, the traditional analysis-modification-synthesis framework is extended to include modulation domain processing. We compensate the noisy modulation spectrum for additive noise distortion by applying the MMSE short-time spectral magnitude esti...

A conventional automatic speech recognizer does not perform well in the presence of multiple sound sources, while human listeners are able to segregate and recognize a signal of interest through auditory scene analysis. We present a computational auditory scene analysis system for separating and recognizing target speech in the presence of competing speech or noise. We estimate, in two stages, ...

This paper considers a single channel speech enhancement algorithm, which is based on our previous work on βorder minimum mean square error (MMSE) spectral estimation. We propose to make β a function of both local and frame signal-to-noise ratios (SNRs) in order to achieve more effective preservation of weak speech components. Moreover, by taking into account the speech-presence uncertainty in ...