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  1. Transactions on Audio, Speech and Language Processing (TASLP)
  2. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 23
  3. Issue 7, July 2015
  4. Rapid language identification
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IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 24
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 23
Issue 12, December 2015
Issue 11, November 2015
Issue 10, Octember 2015
Issue 9, September 2015
Issue 8, August 2015
Issue 7, July 2015
AutoGuitarTab: computer-aided composition of rhythm and lead guitar parts in the tablature space
Rapid language identification
Efficient approximation of head-related transfer functions in subbands for accurate sound localization
An improved framework for recognizing highly imbalanced bilingual code-switched lectures with cross-language acoustic modeling and frame-level language identification
A probabilistic model-based approach for aligning multiple audio sequences
Multitask learning of deep neural networks for low-resource speech recognition
Disambiguating discourse connectives for statistical machine translation
Bounded conditional mean imputation with observation uncertainties and acoustic model adaptation
Bilingual continuous-space language model growing for statistical machine translation
Decoupling word-pair distance and co-occurrence information for effective long history context language modeling
Speech enhancement under low SNR conditions via noise estimation using sparse and low-rank NMF with Kullback–Leibler divergence
Issue 6, June 2015
Issue 5, May 2015
Issue 4, April 2015
Issue 3, March 2015
Issue 2, February 2015
Issue 1, January 2015
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 22

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Rapid language identification

Content Provider ACM Digital Library
Author Travadi, Ruchir Narayanan, Shrikanth S. Van segbroeck, Maarten
Copyright Year 2016
Abstract A critical challenge to automatic language identification (LID) is achieving accurate performance with the shortest possible speech segment in a rapid fashion. The accuracy to correctly identify the spoken language is highly sensitive to the duration of speech and is bounded by the amount of information available. The proposed approach for rapid language identification transforms the utterances to a low dimensional i-vector representation upon which language classification methods are applied. In order to meet the challenges involved in rapidly making reliable decisions about the spoken language, a highly accurate and computationally efficient framework of i-vector extraction is proposed. The LID framework integrates the approach of universal background model (UBM) fused total variability modeling. UBM-fused modeling yields the estimation of a more discriminant, single i-vector space. This way, it is also a computationally more efficient alternative than system level fusion. A further reduction in equal error rate is achieved by training the i-vector model on long duration speech utterances and by the deployment of a robust feature extraction scheme that aims to capture the relevant language cues under various acoustic conditions. Evaluation results on the DARPA RATS data corpus suggest the potential of performing successful automated language identification at the level of one second of speech or even shorter duration.
Starting Page 1118
Ending Page 1129
Page Count 12
File Format PDF
ISSN 23299290
e-ISSN 23299304
DOI 10.1109/TASLP.2015.2419978
Volume Number 23
Issue Number 7
Journal IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-07-01
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword I-vector Noise robustness Short-duration speech Total variability modeling Universal background model (UBM) fusion
Content Type Text
Resource Type Article
Subject Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology
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