Journal ArticleDOI
Efficient Multiple Kernel Support Vector Machine Based Voice Activity Detection
Ji Wu,Xiao-Lei Zhang +1 more
Reads0
Chats0
TLDR
The experimental results show that the proposed MK-SVM method not only leads to better global performances by taking the advantages of multiple features but also has a low computational complexity.Abstract:
In this letter, we propose a multiple kernel support vector machine (MK-SVM) method for multiple feature based VAD. To make the MK-SVM based VAD practical, we adapt the multiple kernel learning (MKL) thought to an efficient cutting-plane structural SVM solver. We further discuss the performances of the MK-SVM with two different optimization objectives, in terms of minimum classification errors (MCE) and improvement of receiver operating characteristic (ROC) curves. Our experimental results show that the proposed method not only leads to better global performances by taking the advantages of multiple features but also has a low computational complexity.read more
Citations
More filters
Journal ArticleDOI
Deep Belief Networks Based Voice Activity Detection
Xiao-Lei Zhang,Ji Wu +1 more
TL;DR: Extensive experimental results on the AURORA2 corpus show that the DBN-based VAD not only outperforms eleven referenced VADs, but also can meet the real-time detection demand of VAD.
Journal ArticleDOI
The LOCATA Challenge: Acoustic Source Localization and Tracking
Christine Evers,Heinrich W. Lollmann,Heinrich Mellmann,Alexander Schmidt,Hendrik Barfuss,Patrick A. Naylor,Walter Kellermann +6 more
TL;DR: The LOCAlization and Tracking Challenge (LOCATA) as discussed by the authors is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking.
Proceedings ArticleDOI
Denoising deep neural networks based voice activity detection
Xiao-Lei Zhang,Ji Wu +1 more
TL;DR: Experimental results show that the proposed denoising-deep-neural-network (DDNN) based VAD not only outperforms the DBN-based VAD but also shows an apparent performance improvement of the deep layers over shallower layers.
Journal ArticleDOI
Voice Activity Detection Via Noise Reducing Using Non-Negative Sparse Coding
Peng Teng,Yunde Jia +1 more
TL;DR: This letter presents a voice activity detection (VAD) approach using non-negative sparse coding to improve the detection performance in low signal-to-noise ratio (SNR) conditions and demonstrates that the VAD approach has a good performance inLow SNR conditions.
Journal ArticleDOI
A review on speech processing using machine learning paradigm
TL;DR: The performance of several machine learning techniques is validated for speech emotion recognition application on Berlin EmoDB database and the broad application areas and challenges in machine learning for speech processing are given.
References
More filters
Journal ArticleDOI
Learning the Kernel Matrix with Semidefinite Programming
Gert R. G. Lanckriet,Nello Cristianini,Peter L. Bartlett,Laurent El Ghaoui,Michael I. Jordan +4 more
TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
Proceedings ArticleDOI
Training linear SVMs in linear time
TL;DR: A Cutting Plane Algorithm for training linear SVMs that provably has training time 0(s,n) for classification problems and o(sn log (n)) for ordinal regression problems and several orders of magnitude faster than decomposition methods like svm light for large datasets.
Proceedings Article
The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions
David Pearce,Hans-Günter Hirsch +1 more
TL;DR: A database designed to evaluate the performance of speech recognition algorithms in noisy conditions and recognition results are presented for the first standard DSR feature extraction scheme that is based on a cepstral analysis.
Journal ArticleDOI
Cutting-plane training of structural SVMs
TL;DR: This paper explores how cutting-plane methods can provide fast training not only for classification SVMs, but also for structural SVMs and presents an extensive empirical evaluation of the method applied to binary classification, multi-class classification, HMM sequence tagging, and CFG parsing.
Proceedings ArticleDOI
A support vector method for multivariate performance measures
TL;DR: An algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea and all measures that can be computed from the contingency table are given.