Distortion measures for speech processing

Gray, Robert M.; Buzo, A.; Gray, Alfred; Matsuyama, Yasuo

doi:10.1109/tassp.1980.1163421

Cited by 335 publications

(123 citation statements)

References 17 publications

Supporting

Mentioning

121

Contrasting

Unclassified

Order By: Relevance

“…Prior to the application of Segmental SNR is usually defined by the mean value of the individual SNR measurements [using (20)] over the frames of the sentence. Segmental SNR is known to be more strongly correlated with subjective quality, and is similar in that sense to the performance of the Itakura-Saito distance measure [22]. However, total output SNR is more robust to the presence of low energy regions (frames), or to frames for which the energy of is small.…”

Section: Comparison With Alternative Speech Enhancement Algorithmsmentioning

confidence: 86%

See 1 more Smart Citation

Speech enhancement using a mixture-maximum model

Burshtein

Gannot

2002

IEEE Trans. Speech Audio Process.

View full text Add to dashboard Cite

Abstract-We present a spectral domain, speech enhancement algorithm. The new algorithm is based on a mixture model for the short time spectrum of the clean speech signal, and on a maximum assumption in the production of the noisy speech spectrum. In the past this model was used in the context of noise robust speech recognition. In this paper we show that this model is also effective for improving the quality of speech signals corrupted by additive noise. The computational requirements of the algorithm can be significantly reduced, essentially without paying performance penalties, by incorporating a dual codebook scheme with tied variances. Experiments, using recorded speech signals and actual noise sources, show that in spite of its low computational requirements, the algorithm shows improved performance compared to alternative speech enhancement algorithms.Index Terms-Gaussian mixture model, MIXMAX model, speech enhancement.

show abstract

Section: Comparison With Alternative Speech Enhancement Algorithmsmentioning

confidence: 86%

“…These distortion measures are known to be correlated with the subjective perception of speech quality [22].…”

Section: Comparison With Alternative Speech Enhancement Algorithmsmentioning

confidence: 99%

Speech enhancement using a mixture-maximum model

Burshtein

Gannot

2002

IEEE Trans. Speech Audio Process.

View full text Add to dashboard Cite

show abstract

“…There are several methods that have been proposed to estimate R x (f ) [1], and the diagonal loading technique is one of the robust algorithms aiming at avoiding the non-singularity in (16), which leads to a norm-constrained FMV (NCFMV) as shown in (17) [3]:…”

Section: Fmv With a Single Avsmentioning

confidence: 99%

“…2. Log spectral deviation (LSD), which is used to measure the speech distortion and defined as [16] LSD…”

Section: Simulation Studymentioning

confidence: 99%

Speech enhancement with an acoustic vector sensor: an effective adaptive beamforming and post-filtering approach

Zou

Wang

et al. 2014

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

Speech enhancement has an increasing demand in mobile communications and faces a great challenge in a real ambient noisy environment. This paper develops an effective spatial-frequency domain speech enhancement method with a single acoustic vector sensor (AVS) in conjunction with minimum variance distortionless response (MVDR) spatial filtering and Wiener post-filtering (WPF) techniques. In remote speech applications, the MVDR spatial filtering is effective in suppressing the strong spatial interferences and the Wiener post-filtering is considered as a popular and powerful estimator to further suppress the residual noise if the power spectral density (PSD) of target speech can be estimated properly. With the favorable directional response of the AVS together with the trigonometric relations of the steering vectors, the closed-form estimation of the signal PSDs is derived and the frequency response of the optimal Wiener post-filter is determined accordingly. Extensive computer simulations and a real experiment in an anechoic chamber condition have been carried out to evaluate the performance of the proposed algorithm. Simulation results show that the proposed method offers good ability to suppress the spatial interference while maintaining comparable log spectral deviation and perceptual evaluation of speech quality performance compared with the conventional methods with several objective measures. Moreover, a single AVS solution is particularly attractive for hands-free speech applications due to its compact size.

show abstract

“…An extensive discussion of these distances is given by Gray, Buzo, Gray, and Matsuyama (1980). The notation 12 L is chosen to emphasize the distinction between that distance and the L 2 spectral density distance 0.5 2…”

Section: Distances Between Spectral Densities 22mentioning

confidence: 99%

Modern Empirical Statistical Spectral Analysis

Parzen

1981

Underwater Acoustics and Signal Processing

View full text Add to dashboard Cite

Distortion measures for speech processing

Cited by 335 publications

References 17 publications

Speech enhancement using a mixture-maximum model

Speech enhancement using a mixture-maximum model

Speech enhancement with an acoustic vector sensor: an effective adaptive beamforming and post-filtering approach

Modern Empirical Statistical Spectral Analysis

Contact Info

Product

Resources

About