This paper describes a novel two-stage dereverberation feature enhancement method for noise-robust automatic speech recognition. In the first stage, an estimate of the dereverberated speech is generated by matching the distribution of the observed reverberant speech to that of clean speech, in a decorrelated transformation domain that has a long temporal context in order to address the effects ...

In this paper, the improved noise tracking algorithm for speech enhancement is proposed. This method is used to detect the speech presence probability based on chi square distribution. During speech presence period, the time varying smoothing factor is adjusted. In addition, the estimated noise variance is recursively smoothed then averaged for various noises. This proposed method can track the...

We have previously identified neurons tuned to spectral contrast of wideband sounds in auditory cortex of awake marmoset monkeys. Because additive noise alters the spectral contrast of speech, contrast-tuned neurons, if present in human auditory cortex, may aid in extracting speech from noise. Given that this cortical function may be underdeveloped in individuals with sensorineural hearing loss...

The goal in speech enhancement is to obtain an estimateof clean speech starting from the noisy signal by minimizing a chosendistortion measure, which results in an estimate that depends onthe unknown clean signal or its statistics. Since access to suchprior knowledge is limited or not possible in practice, one hasto estimate the clean signal statistics. In this paper, we dev...

Nonacoustic sensors such as the general electromagnetic motion sensor (GEMS), the physiological microphone (P-mic), and the electroglottograph (EGG) offer multimodal approaches to speech processing and speaker and speech recognition. These sensors provide measurements of functions of the glottal excitation and, more generally, of the vocal tract articulator movements that are relatively immune ...

A speech enhancement method employing sparse reconstruction of the power spectral density is proposed. The overcomplete dictionary of the power spectral density is learned by approximation K-singular value decomposition algorithm with non negative constraint. The power spectral density of clean speech signal is reconstructed by least angle regression method with a norm termination rule, and the...

A new Time-Frequency (TF) representation of speech signal is introduced and used for speech enhancement. TF representation and speech enhancement algorithm are both based on perceptual properties of human auditory system in which the concept of band analysis is exploited. TF representation is carried out by the means of analytic decomposition of speech signal in the hearing Critical Bands (CB) ...

This paper presents the Multi-Channel Multi-Pulse (MCMP) algorithm for the enhancement of speech degraded by reverberations and additive noise. The enhanced speech is synthesized from a sequence of impulses exciting a linear predictive lter. The excitation signal is computed from a nonlinear process which uses impulse clustering of the multi-channel speech data to discriminate portions of the l...

In-Car speech recognition will be pervasive over the coming years. The goal of speech enhancement is to increase the quality and intelligibility of speech in a noisy environment. The focus of the present research is to evaluate the effect of speech enhancement on the intelligibility of spoken language in a moving vehicle. Here, an ECoS network is used as a model to evaluate the intelligibility....