Statistical-model-based speech enhancement systems

By Yariv Ephraim. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Li Deng
2. Yoshua Bengio
3. Dong Yu
4. Sergey Levine
5. Bo Li
6. Jianfeng Gao
7. Santiago Pascual
8. Trevor Darrell

Citations in the corpus

• SEGAN: Speech Enhancement Generative Adversarial Network (2017, methodology): “Classic speech enhancement methods are spectral subtraction [6], Wiener filtering [7], statistical model-based meth-ods [8], and subspace algorithms [9, 10].”
• Survey of the State of the Art in Human Language Technology (2010, methodology): “structural models for adaptation to acoustical variability assume that speech is corrupted either by additive noise with an unknown power spectrum (Porter & Boll, 1984; Ephraim, 1992; Erell & Weintraub, 1990; Gales & Young, 1992; Lockwood, Boudy, et al., 1992; Bellegarda, de Souza, et al., 1992), or”
• Hidden Markov processes (2002, methodology): “Several other estimators for the signal from were developed [105].”
• Speech enhancement with missing data techniques using recurrent neural networks (2004, methodology): “The enhancement system consists of two stages: the first stage uses a Recurrent Neural Network, which is supplied with noisy speech and produces enhanced speech; whereas the second stage uses missing data techniques to further improve the quality of enhanced speech.”
• Speech Denoising and Dereverberation Using Probabilistic Models (2000, methodology): “These methods often use a probabilistic framework with statistical models of a single speech signal corrupted by Gaussian noise [6, 8].”
• New insights into the noise reduction Wiener filter (2006, methodology): “A variety of approaches have been developed, including Wiener filter [3], [14]–[19], spectral or cepstral restoration [17], [20]–[27], signal subspace [28]–[35], parametric-model-based method [36]–[38], and statistical-model-based method [5], [39]–[46].”