2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第8页

A study of discriminative feature extraction for i-vector based acoustic sniffing in IVN acoustic model training IVN声学模型训练中基于i向量的声学嗅探判别特征提取研究

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288814

Yu Zhang, Jian Xu, Zhijie Yan, Qiang Huo

引用次数: 0

Multichannel speech dereverberation and separation with optimized combination of linear and non-linear filtering 多通道语音去噪和分离与线性和非线性滤波的优化组合

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288809

M. Togami, Y. Kawaguchi, Ryu Takeda, Y. Obuchi, N. Nukaga

引用次数: 10

Trade-off evaluation for speech enhancement algorithms with respect to the a priori SNR estimation 基于先验信噪比估计的语音增强算法的权衡评估

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288957

Pei Chee Yong, S. Nordholm, H. H. Dam

引用次数: 6

Normalized amplitude modulation features for large vocabulary noise-robust speech recognition 大词汇噪声鲁棒语音识别的归一化调幅特征

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288824

V. Mitra, H. Franco, M. Graciarena, Arindam Mandal

{"title":"Normalized amplitude modulation features for large vocabulary noise-robust speech recognition","authors":"V. Mitra, H. Franco, M. Graciarena, Arindam Mandal","doi":"10.1109/ICASSP.2012.6288824","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288824","url":null,"abstract":"Background noise and channel degradations seriously constrain the performance of state-of-the-art speech recognition systems. Studies comparing human speech recognition performance with automatic speech recognition systems indicate that the human auditory system is highly robust against background noise and channel variabilities compared to automated systems. A traditional way to add robustness to a speech recognition system is to construct a robust feature set for the speech recognition model. In this work, we present an amplitude modulation feature derived from Teager's nonlinear energy operator that is power normalized and cosine transformed to produce normalized modulation cepstral coefficient (NMCC) features. The proposed NMCC features are compared with respect to state-of-the-art noise-robust features in Aurora-2 and a renoised Wall Street Journal (WSJ) corpus. The WSJ word-recognition experiments were performed on both a clean and artificially renoised WSJ corpus using SRI's DECIPHER large vocabulary speech recognition system. The experiments were performed under three train-test conditions: (a) matched, (b) mismatched, and (c) multi-conditioned. The Aurora-2 digit recognition task was performed using the standard HTK recognizer distributed with Aurora-2. Our results indicate that the proposed NMCC features demonstrated noise robustness in almost all the training-test conditions of renoised WSJ data and also improved digit recognition accuracies for Aurora-2 compared to the MFCCs and state-of-the-art noise-robust features.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"4117-4120"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85498682","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: 104

A family of Bounded Component Analysis algorithms 一类有界分量分析算法

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288270

A. Erdogan

引用次数: 19

Automatic generation of synthesizable hardware implementation from high level RVC-cal description 从高级RVC-cal描述自动生成可合成的硬件实现

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288199

Khaled Jerbi, M. Raulet, O. Déforges, M. Abid

{"title":"Automatic generation of synthesizable hardware implementation from high level RVC-cal description","authors":"Khaled Jerbi, M. Raulet, O. Déforges, M. Abid","doi":"10.1109/ICASSP.2012.6288199","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288199","url":null,"abstract":"Data process algorithms are increasing in complexity especially for image and video coding. Therefore, hardware development using directly hardware description languages (HDL) such as VHDL or Verilog is a difficult task. Current research axes in this context are introducing new methodologies to automate the generation of such descriptions. In our work we adopted a high level and target-independent language called CAL (Caltrop Actor Language). This language is associated with a set of tools to easily design dataflow applications and also a hardware compiler to automatically generate the implementation. Before the modifications presented in this paper, the existing CAL hardware back-end did not support some high-level features of the CAL language. Consequently, high-level designed actors had to be manually transformed to be synthesizable. In this paper, we introduce a general automatic transformation of CAL descriptions to make these structures compliant and synthesizable. This transformation analyses the CAL code, detects the target features and makes the required changes to obtain synthesizable code while keeping the same application behavior. This work resolves the main bottleneck of the hardware generation flow from CAL designs.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"2 1","pages":"1597-1600"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85692792","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: 13

Graph spectral compressed sensing for sensor networks 用于传感器网络的图谱压缩感知

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288515

Xiaofan Zhu, M. Rabbat

引用次数: 56

Noncoherent misbehavior detection in space-time coded cooperative networks 时空编码合作网络中的非相干异常行为检测

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288561

Li-Chung Lo, Zhao-Jie Wang, Wan-Jen Huang

引用次数: 11

Detecting passive eavesdroppers in the MIMO wiretap channel 在MIMO窃听信道中检测无源窃听者

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288501

A. Mukherjee, A. L. Swindlehurst

引用次数: 147

MLLR transforms of self-organized units as features in speaker recognition 自组织单元的MLLR变换作为特征在说话人识别中

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-03-25 DOI: 10.1109/ICASSP.2012.6288891

M. Siu, Omer Lang, H. Gish, S. Lowe, Arthur Chan, O. Kimball

{"title":"MLLR transforms of self-organized units as features in speaker recognition","authors":"M. Siu, Omer Lang, H. Gish, S. Lowe, Arthur Chan, O. Kimball","doi":"10.1109/ICASSP.2012.6288891","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288891","url":null,"abstract":"Using speaker adaptation parameters, such as maximum likelihood linear regression (MLLR) adaptation matrices, as features for speaker recognition (SR) has been shown to perform well and can also provide complementary information for fusion with other acoustic-based SR systems, such as GMM-based systems. In order to estimate the adaptation parameters, a speech recognizer in the SR domain is required which in turn requires transcribed training data for recognizer training. This limits the approach only to domains where training transcriptions are available. To generalize the adaptation parameter approach to domains without transcriptions, we propose the use of self-organized unit recognizers that can be trained without supervision (or transcribed data). We report results on the 2002 NIST speaker recognition evaluation (SRE2002) extended data set and show that using MLLR parameters estimated from SOU recognizers give comparable performance to systems using a matched recognizers. SOU recognizers also outperform those using cross-lingual recognizers. When we fused the SOU- and word recognizers, SR equal error rate (EER) can be reduced by another 15%. This suggests SOU recognizers can be useful whether or not transcribed data for recognition training are available.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"267 1","pages":"4385-4388"},"PeriodicalIF":0.0,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72910724","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: 2