IEEE Transactions on Audio Speech and Language Processing最新文献_第4页

Convergence Analysis of Narrowband Feedback Active Noise Control System With Imperfect Secondary Path Estimation 次要路径估计不完全的窄带反馈有源噪声控制系统收敛性分析

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-11-01 DOI: 10.1109/TASL.2013.2277934

Liang Wang, W. Gan, Andy W. H. Khong, S. Kuo

引用次数: 21

Unsupervised Spoken Language Understanding for a Multi-Domain Dialog System 多域对话系统的无监督口语理解

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-11-01 DOI: 10.1109/TASL.2013.2280212

Donghyeon Lee, Minwoo Jeong, Kyungduk Kim, Seonghan Ryu, G. G. Lee

{"title":"Unsupervised Spoken Language Understanding for a Multi-Domain Dialog System","authors":"Donghyeon Lee, Minwoo Jeong, Kyungduk Kim, Seonghan Ryu, G. G. Lee","doi":"10.1109/TASL.2013.2280212","DOIUrl":"https://doi.org/10.1109/TASL.2013.2280212","url":null,"abstract":"This paper proposes an unsupervised spoken language understanding (SLU) framework for a multi-domain dialog system. Our unsupervised SLU framework applies a non-parametric Bayesian approach to dialog acts, intents and slot entities, which are the components of a semantic frame. The proposed approach reduces the human effort necessary to obtain a semantically annotated corpus for dialog system development. In this study, we analyze clustering results using various evaluation metrics for four dialog corpora. We also introduce a multi-domain dialog system that uses the unsupervised SLU framework. We argue that our unsupervised approach can help overcome the annotation acquisition bottleneck in developing dialog systems. To verify this claim, we report a dialog system evaluation, in which our method achieves competitive results in comparison with a system that uses a manually annotated corpus. In addition, we conducted several experiments to explore the effect of our approach on reducing development costs. The results show that our approach be helpful for the rapid development of a prototype system and reducing the overall development costs.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2280212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62892553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Active-Set Newton Algorithm for Overcomplete Non-Negative Representations of Audio 音频过完全非负表示的主动集牛顿算法

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-11-01 DOI: 10.1109/TASL.2013.2263144

T. Virtanen, J. Gemmeke, B. Raj

引用次数: 70

Robust SVD-Based Audio Watermarking Scheme With Differential Evolution Optimization 基于差分进化优化的鲁棒svd音频水印方案

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-11-01 DOI: 10.1109/TASL.2013.2277929

B. Lei, I. Soon, Ee-Leng Tan

{"title":"Robust SVD-Based Audio Watermarking Scheme With Differential Evolution Optimization","authors":"B. Lei, I. Soon, Ee-Leng Tan","doi":"10.1109/TASL.2013.2277929","DOIUrl":"https://doi.org/10.1109/TASL.2013.2277929","url":null,"abstract":"In this paper, a robust audio watermarking scheme based on singular value decomposition (SVD) and differential evolution (DE) using dither modulation (DM) quantization algorithm is proposed. Two novel SVD-based algorithms, lifting wavelet transform (LWT)-discrete cosine transform (DCT)-SVD and discrete wavelet transform (DWT)-DCT-SVD, are developed for audio copyright protection. In our method, LWTDWT is first applied to decompose the host signal and obtain the corresponding approximate coefficients followed by DCT to take advantage of “energy compaction” property. SVD is further performed to acquire the singular values and enhance the robustness of the scheme. The adaptive DM quantization is adopted to quantize the singular values and embed the watermark. To withstand desynchronization attacks, synchronization code is inserted using audio statistical characteristics. Furthermore, the conflicting problem of robustness and imperceptibility is effectively resolved by the DE optimization. Simulation results demonstrate that both the LWT-DCT-SVD and DWT-DCT-SVD methods not only have good imperceptibility performance, but also resist general signal processing, hybrid and desynchronization attacks. Compared with the previous DWT-DCT, support vector regression (SVR)-DWT-DCT and DWT-SVD methods, our method obtains more robustness against the selected attacks.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2277929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62892113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 106

Robust Segments Detector for De-Synchronization Resilient Audio Watermarking 鲁棒片段检测器用于去同步弹性音频水印

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-11-01 DOI: 10.1109/TASL.2013.2279312

Chi-Man Pun, Xiaochen Yuan

{"title":"Robust Segments Detector for De-Synchronization Resilient Audio Watermarking","authors":"Chi-Man Pun, Xiaochen Yuan","doi":"10.1109/TASL.2013.2279312","DOIUrl":"https://doi.org/10.1109/TASL.2013.2279312","url":null,"abstract":"A robust feature points detector for invariant audio watermarking is proposed in this paper. The audio segments centering at the detected feature points are extracted for both watermark embedding and extraction. These feature points are invariant to various attacks and will not be changed much for maintaining high auditory quality. Besides, high robustness and inaudibility can be achieved by embedding the watermark into the approximation coefficients of Stationary Wavelet Transform (SWT) domain, which is shift invariant. The spread spectrum communication technique is adopted to embed the watermark. Experimental results show that the proposed Robust Audio Segments Extractor (RASE) and the watermarking scheme are not only robust against common audio signal processing, such as low-pass filtering, MP3 compression, echo addition, volume change, and normalization; and distortions introduced in Stir-mark benchmark for Audio; but also robust against synchronization geometric distortions simultaneously, such as resample time-scale modification (TSM) with scaling factors up to ±50%, pitch invariant TSM by ±50%, and tempo invariant pitch shifting by ±50%. In general, the proposed scheme can well resist various attacks by the joint RASE and SWT approach, which performs much better comparing with the existing state-of-the art methods.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2279312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62892423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 51

Quality Measure Functions for Calibration of Speaker Recognition Systems in Various Duration Conditions 不同持续时间条件下说话人识别系统校准的质量测量函数

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-11-01 DOI: 10.1109/TASL.2013.2279332

M. I. Mandasari, R. Saeidi, Mitchell McLaren, D. V. Leeuwen

引用次数: 77

Semi-Blind Noise Extraction Using Partially Known Position of the Target Source 利用部分已知目标源位置的半盲噪声提取

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2264674

Zbyněk Koldovský, J. Málek, P. Tichavský, F. Nesta

引用次数: 31

Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization 基于非负矩阵分解的有监督和无监督语音增强

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2270369

N. Mohammadiha, P. Smaragdis, A. Leijon

{"title":"Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization","authors":"N. Mohammadiha, P. Smaragdis, A. Leijon","doi":"10.1109/TASL.2013.2270369","DOIUrl":"https://doi.org/10.1109/TASL.2013.2270369","url":null,"abstract":"Reducing the interference noise in a monaural noisy speech signal has been a challenging task for many years. Compared to traditional unsupervised speech enhancement methods, e.g., Wiener filtering, supervised approaches, such as algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced speech signals. However, the main practical difficulty of these approaches is that for each noise type a model is required to be trained a priori. In this paper, we investigate a new class of supervised speech denoising algorithms using nonnegative matrix factorization (NMF). We propose a novel speech enhancement method that is based on a Bayesian formulation of NMF (BNMF). To circumvent the mismatch problem between the training and testing stages, we propose two solutions. First, we use an HMM in combination with BNMF (BNMF-HMM) to derive a minimum mean square error (MMSE) estimator for the speech signal with no information about the underlying noise type. Second, we suggest a scheme to learn the required noise BNMF model online, which is then used to develop an unsupervised speech enhancement system. Extensive experiments are carried out to investigate the performance of the proposed methods under different conditions. Moreover, we compare the performance of the developed algorithms with state-of-the-art speech enhancement schemes using various objective measures. Our simulations show that the proposed BNMF-based methods outperform the competing algorithms substantially.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2270369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62890890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 370

A Direct Masking Approach to Robust ASR 鲁棒ASR的直接掩蔽方法

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2263802

William Hartmann, A. Narayanan, E. Fosler-Lussier, Deliang Wang

引用次数: 43

Feature Enhancement With Joint Use of Consecutive Corrupted and Noise Feature Vectors With Discriminative Region Weighting 结合区分区域加权的连续损坏和噪声特征向量的特征增强

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2270407

Masayuki Suzuki, Takuya Yoshioka, Shinji Watanabe, N. Minematsu, K. Hirose

{"title":"Feature Enhancement With Joint Use of Consecutive Corrupted and Noise Feature Vectors With Discriminative Region Weighting","authors":"Masayuki Suzuki, Takuya Yoshioka, Shinji Watanabe, N. Minematsu, K. Hirose","doi":"10.1109/TASL.2013.2270407","DOIUrl":"https://doi.org/10.1109/TASL.2013.2270407","url":null,"abstract":"This paper proposes a feature enhancement method that can achieve high speech recognition performance in a variety of noise environments with feasible computational cost. As the well-known Stereo-based Piecewise Linear Compensation for Environments (SPLICE) algorithm, the proposed method learns piecewise linear transformation to map corrupted feature vectors to the corresponding clean features, which enables efficient operation. To make the feature enhancement process adaptive to changes in noise, the piecewise linear transformation is performed by using a subspace of the joint space of corrupted and noise feature vectors, where the subspace is chosen such that classes (i.e., Gaussian mixture components) of underlying clean feature vectors can be best predicted. In addition, we propose utilizing temporally adjacent frames of corrupted and noise features in order to leverage dynamic characteristics of feature vectors. To prevent overfitting caused by the high dimensionality of the extended feature vectors covering the neighboring frames, we introduce regularized weighted minimum mean square error criterion. The proposed method achieved relative improvements of 34.2% and 22.2% over SPLICE under the clean and multi-style conditions, respectively, on the Aurora 2 task.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2270407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62890737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7