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    • Ma, N. (8)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Acoustic screening for obstructive sleep apnea in home environments based on deep neural networks Romero, H.E.; Ma, N.; Brown, G.. - : Institute of Electrical and Electronics Engineers (IEEE), 2022 BASE Show details 2 Robust binaural localization of a target sound source by combining spectral source models and deep neural networks Ma, N.; Gonzalez, J.; Brown, G.J.. - : Institute of Electrical and Electronics Engineers, 2018 BASE Show details 3 Exploiting Deep Neural Networks and Head Movements for Robust Binaural Localisation of Multiple Sources in Reverberant Environments Ma, N.; May, T.; Brown, G.J.. - : Institute of Electrical and Electronics Engineers, 2017 BASE Show details 4 Spectral Reconstruction and Noise Model Estimation Based on a Masking Model for Noise Robust Speech Recognition Gonzalez, J.A.; Gómez, A.M.; Peinado, A.M.; Ma, N.; Barker, J.. - : Springer Verlag (Germany), 2017 Abstract: An effective way to increase noise robustness in automatic speech recognition (ASR) systems is feature enhancement based on an analytical distortion model that describes the effects of noise on the speech features. One of such distortion models that has been reported to achieve a good trade-off between accuracy and simplicity is the masking model. Under this model, speech distortion caused by environmental noise is seen as a spectral mask and, as a result, noisy speech features can be either reliable (speech is not masked by noise) or unreliable (speech is masked). In this paper, we present a detailed overview of this model and its applications to noise robust ASR. Firstly, using the masking model, we derive a spectral reconstruction technique aimed at enhancing the noisy speech features. Two problems must be solved in order to perform spectral reconstruction using the masking model: (1) mask estimation, i.e. determining the reliability of the noisy features, and (2) feature imputation, i.e. estimating speech for the unreliable features. Unlike missing data imputation techniques where the two problems are considered as independent, our technique jointly addresses them by exploiting a priori knowledge of the speech and noise sources in the form of a statistical model. Secondly, we propose an algorithm for estimating the noise model required by the feature enhancement technique. The proposed algorithm fits a Gaussian mixture model to the noise by iteratively maximising the likelihood of the noisy speech signal so that noise can be estimated even during speech-dominating frames. A comprehensive set of experiments carried out on the Aurora-2 and Aurora-4 databases shows that the proposed method achieves significant improvements over the baseline system and other similar missing data imputation techniques. URL: http://eprints.whiterose.ac.uk/112035/1/cssp-2016.pdf http://eprints.whiterose.ac.uk/112035/ https://doi.org/10.1007/s00034-016-0480-7 BASE Hide details 5 Benefits to Speech Perception in Noise From the Binaural Integration of Electric and Acoustic Signals in Simulated Unilateral Deafness Kitterick, P.T.; Morris, S.; Ma, N.. - : Lippincott, Williams & Wilkins, 2016 BASE Show details 6 Speech localisation in a multitalker mixture by humans and machines Ma, N.; Brown, G.J.. - : ISCA, 2016 BASE Show details 7 Exploiting correlogram structure for robust speech recognition with multiple speech sources Ma, N.; Green, P.; Barker, J.. - : Elsevier B.V., 2007 BASE Show details 8 SPECIAL SECTION PAPERS - Speech Enhancement Using a Masking Threshold Constrained Kalman Filter and Its Heuristic Implementations Ma, N.; Bouchard, M.; Goubran, R.A. In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 14 (2006) 1, 19-32 OLC Linguistik Show details

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