This paper examines the forward masking on the generalized logarithmic scale for robust speech recognition to both additive and convolutional noise. The forward masking in the dynamic cepstral (DyC) representation is based upon subtraction of a masking pattern from a current spectrum on a logarithmic spectral domain, whereas the proposed method intends to make a compromise between the logarithm...

1 MULTI-CHANNEL SPEECH ENHANCEMENT IN A CAR ENVIRONMENT USING WIENER FILTERING AND SPECTRAL SUBTRACTION Joerg Meyer and Klaus Uwe Simmer University of Bremen, FB{1, Dept. of Telecommunications P.O. Box 33 04 40, D-28334 Bremen, Germany, Fax: +(49)-421/218-3341, e-mail: [email protected] ABSTRACT This paper presents a multichannel-algorithm for speech enhancement for hands{free telephone ...

In this paper the performance of a new feature set, Locally Normalized Cepstral Coefficients (LNCC) is evaluated for a speaker verification task with short testing utterances in additive noise. The results presented here show that LNCC outperforms baseline MFCC features when SNR is lower than 15 dB. The average relative reduction in EER achieved by LNCC is 33%. The use of LNCC in combination wi...

Automatic speech recognition performance tends to be degraded in noisy conditions. Spectral subtraction is a simple, popular approach of noise compensation. In conventional spectral subtraction [1, 2], noise statistics are updated during speech gaps and subtracted from a corrupt signal during speech intervals. Some means of explicit speech, non-speech detection is therefore essential. Recent pr...

Investigating Speaker Verification in real-world noisy environments, a novel feature extraction process suitable for suppression of time-varying noise is compared with a fine-tuned spectral subtraction method. The proposed feature extraction process is based on approximating the clean speech and the noise spectral magnitude with a mixture of Gaussian probability density functions (pdfs) by usin...

This paper proposes an efficient reconfigurable hardware design for speech enhancement based on multi band spectral subtraction algorithm and involving both magnitude and phase components. Our proposed design is novel as it estimates environmental noise from speech adaptively utilizing both magnitude and phase components of the speech spectrum. We performed multi-band spectral subtraction by di...

Additive noise generates important losses in automatic speech recognition systems. In this paper, we show that one of the causes contributing to these losses is the fact that conventional recognisers take into consideration feature values that are outliers. The method that we call bounded-distance HMM is a suitable method to avoid that outliers contribute to the recogniser decision. However, th...

To mitigate the performance limitations caused by the constant spectral order β in the traditional spectral subtraction methods, we previously presented an adaptive β-order generalized spectral subtraction (GSS) in which the spectral order β is updated in a heuristic way [10]. In this paper, we propose a psychoacoustically-motivated adaptive β-order GSS, by considering that different frequency ...