Results 211 to 220 of about 31,514 (254)

Compressive speech enhancement

Speech Communication, 2013
This paper presents an alternative approach to speech enhancement by using compressed sensing (CS). CS is a new sampling theory, which states that sparse signals can be reconstructed from far fewer measurements than the Nyquist sampling. As such, CS can be exploited to reconstruct only the sparse components (e.g., speech) from the mixture of sparse and
Siow Yong Low, Duc-Son Pham 0001, Svetha Venkatesh   +2 more
openaire   +3 more sources

Modification on LSA speech enhancement for speech recognition

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
Speech recognition performance deteriorates in face of unknown noise. Speech enhancement offers a solution by reducing the noise in speech at runtime. However, it also introduces artificial distortions to the speech signals. In this paper, we aim at reducing the artifacts that has adverse effects on speech recognition.
Chang Huai You, Bin Ma 0001, Chongjia Ni
openaire   +1 more source

Speech Enhancement Using Transient Speech Components

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
This paper describes an algorithm to decompose speech into tonal, transient, and residual components. The algorithm uses an MDCT-based hidden Markov chain model to isolate the tonal component and a wavelet-based hidden Markov tree model to isolate the transient component.
Charturong Tantibundhit, J. Robert Boston, Ching-Chung Li, John D. Durrant, Susan Shaiman, Kristie Kovacyk, Amro El-Jaroudi   +6 more
openaire   +1 more source

Application of speech conversion to alaryngeal speech enhancement

IEEE Transactions on Speech and Audio Processing, 1997
Two existing speech conversion algorithms were modified and used to enhance alaryngeal speech. The modifications were aimed at reducing the spectral distortion (bandwidth increase) in a vector-quantization (VQ) based system and the spectral discontinuity in a linear multivariate regression (LMR) based system.
Ning Bi, Yingyong Qi
openaire   +1 more source

Speech Enhancement With Inventory Style Speech Resynthesis

IEEE Transactions on Audio, Speech, and Language Processing, 2010
We present a new method for the enhancement of speech. The method is designed for scenarios in which targeted speaker enrollment as well as system training within the typical noise environment are feasible. The proposed procedure is fundamentally different from most conventional and state-of-the-art denoising approaches.
Xiaoqiang Xiao, Robert M. Nickel
openaire   +1 more source

Speech enhancement for telephony name speech recognition

2008 IEEE International Conference on Multimedia and Expo, 2008
In this paper, we investigate the contribution of the speech enhancement to telephony name speech recognition system that has been of the feature enhancement in noisy environment. Since the masking based subband Kalman filtering (MSKF) method has been shown to have good performance over many existing speech enhancement methods for human listening, we ...
Chang Huai You, Susanto Rahardja, Haizhou Li 0001   +2 more
openaire   +1 more source

Environment-Optimized Speech Enhancement

IEEE Transactions on Audio, Speech, and Language Processing, 2008
In this paper, we present a training-based approach to speech enhancement that exploits the spectral statistical characteristics of clean speech and noise in a specific environment. In contrast to many state-of-the-art approaches, we do not model the probability density function (pdf) of the clean speech and the noise spectra.
Tim Fingscheidt, Suhadi Suhadi, Sorel Stan   +2 more
openaire   +1 more source

Speech enhancement

ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1983
openaire   +2 more sources

Speech Enhancement

2014
  +6 more sources

Speech enhancement in the case of speech recognizers

2nd European Conference on Speech Communication and Technology (Eurospeech 1991), 1991
openaire   +1 more source