A Bayesian framework for robust speech enhancement under varying contexts

Naidu, D. Hanumantha Rao; Srinivasan, Sriram

doi:10.1109/icassp.2012.6288932

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Data-driven speech models, built on the training data of real speech, represent a different 33 way of imposing prior or constraint on the speech to be estimated. Common speech models include 34 vector-quantization (VQ) codebooks (e.g., Naidu and Srinivasan, 2012;Srinivasan et al, 2006), 35 Gaussian mixture models (GMM) (e.g., Kundu et al, 2008), hidden Markov models (HMM) (e.g., 36 Ephraim et al, 1989; Sameti and Deng, 2002;Zhao and Kleijn, 2007), and inventory-based models 37 terms of improved speech recognition and speech enhancement performance.…”

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confidence: 99%

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

Crookes

2014

Computer Speech & Language

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An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

Crookes

2014

Computer Speech & Language

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Section: Introductionmentioning

confidence: 99%

“…A part of this work has been presented in [35]. This papers extends [35] by incorporating memory-based estimation, considers the use of multiple CD models, and presents a detailed experimental analysis for different noise types, input SNRs, and aspects of context. The framework developed is general and can be used for other representations such as mel-frequency cepstrum coefficients, higher resolution PSDs, as well as other models such as GMMs, HMMs, and NMF.…”

Section: Introductionmentioning

confidence: 99%

Robust Bayesian estimation for context-based speech enhancement

Naidu

Srinivasan

2014

J AUDIO SPEECH MUSIC PROC.

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Model-based speech enhancement algorithms that employ trained models, such as codebooks, hidden Markov models, Gaussian mixture models, etc., containing representations of speech such as linear predictive coefficients, mel-frequency cepstrum coefficients, etc., have been found to be successful in enhancing noisy speech corrupted by nonstationary noise. However, these models are typically trained on speech data from multiple speakers under controlled acoustic conditions. In this paper, we introduce the notion of context-dependent models that are trained on speech data with one or more aspects of context, such as speaker, acoustic environment, speaking style, etc. In scenarios where the modeled and observed contexts match, context-dependent models can be expected to result in better performance, whereas context-independent models are preferred otherwise. In this paper, we present a Bayesian framework that automatically provides the benefits of both models under varying contexts. As several aspects of the context remain constant over an extended period during usage, a memory-based approach that exploits information from past data is employed. We use a codebook-based speech enhancement technique that employs trained models of speech and noise linear predictive coefficients as an example model-based approach. Using speaker, acoustic environment, and speaking style as aspects of context, we demonstrate the robustness of the proposed framework for different context scenarios, input signal-to-noise ratios, and number of contexts modeled.

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A novel Bayesian framework for speech enhancement using speech presence uncertainty

Wang

Bao

2015

2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)

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A Bayesian framework for robust speech enhancement under varying contexts

Cited by 5 publications

References 7 publications

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

Robust Bayesian estimation for context-based speech enhancement

A novel Bayesian framework for speech enhancement using speech presence uncertainty

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