Proceedings ArticleDOI
Speech Enhancement from Additive Noise and Channel Distortion - a Corpus-Based Approach
Ji Ming,Danny Crookes +1 more
- pp 2710-2714
TLDR
This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise) based on finding longest matching segments (LMS) from a corpus of clean, wideband speech.Abstract:
This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.read more
Citations
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Proceedings ArticleDOI
HMM-Based Speech Enhancement Using Sub-Word Models and Noise Adaptation
Akihiro Kato,Ben Milner +1 more
TL;DR: Speech quality and intelligibility analysis find triphone models with no grammar, combined with noise adaptation, gives highest performance that outperforms conventional methods of enhancement at low signal-to-noise ratios.
Journal ArticleDOI
Reconstruction-based speech enhancement from robust acoustic features
Philip Harding,Ben Milner +1 more
TL;DR: Objective and subjective tests compare reconstruction-based enhancement with other methods of enhancement and show the proposed method to be highly effective at removing noise.
Dissertation
Hidden Markov model-based speech enhancement
TL;DR: This work proposes a method of model-based speech enhancement that uses a network of HMMs to first decode noisy speech and to then synthesise a set of features that enables a speech production model to reconstruct clean speech.
References
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Journal ArticleDOI
Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator
Yariv Ephraim,David Malah +1 more
TL;DR: In this article, a system which utilizes a minimum mean square error (MMSE) estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the "spectral subtraction" algorithm.
Journal Article
Speech enhancement using a minimum mean square error short-time spectral amplitude estimator
TL;DR: This paper derives a minimum mean-square error STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables, which results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise.
Journal ArticleDOI
Noise power spectral density estimation based on optimal smoothing and minimum statistics
TL;DR: An unbiased noise estimator is developed which derives the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal by minimizing a conditional mean square estimation error criterion in each time step.
Journal ArticleDOI
A statistical model-based voice activity detection
TL;DR: An effective hang-over scheme which considers the previous observations by a first-order Markov process modeling of speech occurrences is proposed which shows significantly better performances than the G.729B VAD in low signal-to-noise ratio (SNR) and vehicular noise environments.
Noise Spectrum Estimation in Adverse Environments: Improved Minima Controlled
Recursive Averaging,Israel Cohen +1 more
TL;DR: It is shown that in nonstationary noise environments and under low SNR conditions, the IMCRA approach is very effective, compared to a competitive method, it obtains a lower estimation error, and when integrated into a speech enhancement system achieves improved speech quality and lower residual noise.