See also:
Dialog Systems in Vehicle Telematics
Feature Compensation Employing Variational Model Composition for Robust Speech Recognition in In-Vehicle Environment W Kim… – … Processing for In-Vehicle Systems and Safety, 2012 – Springer … results prove that the proposed method is considerably more effective at increasing speech recognition performance in time-varying background noise conditions with +20.80% relative improvement in word error rates for the CU- Move real-life in-vehicle corpus, compared to an … Related articles – All 2 versions
In-Vehicle Speech and Noise Corpora N Krishnamurthy, R Lubag… – … Processing for In-Vehicle …, 2012 – Springer … sniffing: noise knowledge estimation for robust speech systems, IEEE Trans Audio Speech Lang Process 15(2):465-477 6. Hansen JHL, Zhang X, Akbacak M, Yapanel U, Pellom B, Ward W (2003) CU-Move: advances in in-vehicle speech systems for route navigation. … Related articles – All 2 versions
Feature Compensation Employing Model Combination for Robust In-Vehicle Speech Recognition W Kim… – In-Vehicle Corpus and Signal Processing for Driver …, 2009 – Springer … 19.7 Conclusion In this chapter, we evaluated the PCGMM-based feature compensation method on the CU-Move in-vehicle speech corpus which contains a range of back- ground noise observed in real-life in-vehicle conditions. To reduce the … Related articles – All 2 versions
CU-MOVE: advanced in-vehicle speech systems for route navigation [PDF] from buecher.de J Hansen, X Zhang, M Akbacak, U Yapanel… – DSP for in-vehicle and …, 2005 – Springer In this chapter, we present our recent advances in the formulation and development of an in- vehicle hands-free route navigation system. The system is comprised of a multi-microphone array processing front-end, environmental sniffer (for noise analysis), robust speech … Cited by 26 – Related articles – All 6 versions
A kernel mean matching approach for environment mismatch compensation in speech recognition [PDF] from utah.edu A Kumar… – Acoustics Speech and Signal …, 2010 – ieeexplore.ieee.org … 5. Springer Publishing, 2008. [13] JHL Hansen et al., “CU-MOVE: Advanced in-vehicle speech systems for route navigation,” in DSP for In-Vehicle and Mobile Systems, chapter 2. Springer Publishing, 2004. [14] PJMoreno, B … Related articles – All 5 versions
International Large-Scale Vehicle Corpora for Research on Driver Behavior on the Road K Takeda, JHL Hansen, P Boyraz… – Intelligent …, 2011 – ieeexplore.ieee.org … in automobiles. The CU-Move research team at the University of Colorado1 [2]-[4] has collected natural conversational speech from 500 speakers within six cities across the US using an instru- mented vehicle. The CU-Move … Related articles – All 3 versions
Variational noise model composition through model perturbation for robust speech recognition with time-varying background noise W Kim… – Speech Communication, 2011 – Elsevier … Such actual examples can be easily found in the corpora of in-vehicle scenarios such as UTDrive (Angkititrakul et al., 2007 and Angkititrakul et al., 2009) and CU-Move (Hansen et al., 2004), or spoken document retrieval of diverse audio data such as the National Gallery of … Cited by 1 – Related articles – All 4 versions
Analysis of CFA-BF: Novel combined fixed/adaptive beamforming for robust speech recognition in real car environments JHL Hansen… – Speech Communication, 2010 – Elsevier … This paper is organized as follows. In Section 2, we introduce the CU-Move in-vehicle speech database collected for development of in-vehicle route navigation. … 2. CU-Move: in-vehicle speech corpus for interactive speech systems. … Cited by 2 – Related articles – All 3 versions
Feature Compensation Employing Multiple Environmental Models for Robust In-Vehicle Speech Recognition KIM Wooil… – IEICE TRANSACTIONS on Information …, 2008 – search.ieice.org … Summary: An effective feature compensation method is developed for reliable speech recognition in real-life in-vehicle environments. The CU-Move corpus, used for evaluation, contains a range of speech and noise signals collected for a number of speakers under actual … Related articles – Cached – BL Direct – All 7 versions
Missing-feature reconstruction by leveraging temporal spectral correlation for robust speech recognition in background noise conditions W Kim… – Audio, Speech, and Language Processing, …, 2010 – ieeexplore.ieee.org … based method. Performance of the proposed method is evaluated on the TIMIT speech corpus using various types of background noise conditions and the CU- Move in-vehicle speech corpus. Experimental results demonstrate … Cited by 1 – Related articles – All 4 versions
Environment mismatch compensation methods for robust speech recognition A Kumar – 2010 – gradworks.umi.com … car environments–CU-Move and UTDrive. The approaches are also compared empirically with other popular methods existing in the literature. Performance improvements range from 11% to 26% over baseline method for matched and mismatched in-vehicle noisy conditions. … Cached – Library Search
Robust Minimal Variance Distortionless Speech Power Spectra Enhancement Using Order Statistic Filter for Microphone Array [PDF] from pitt.edu T Yu… – Tenth Annual Conference of the International …, 2009 – isca-speech.org … continuous speech database”, NIST, Gaithersburg, MD, prototype as of Dec. 1988. [10] J.Hansen, etc.,”CU-Move:analysis and corpus development for interactive in-vehicle speech systems”,Eurospeech’01, Sept. 2001 1366 Related articles – All 4 versions
Environment mismatch compensation using average eigenspace for speech recognition [PDF] from utah.edu A Kumar… – Ninth Annual Conference of the …, 2008 – isca-speech.org … 5. [13] JHL Hansen, XX Zhang, M. Akbacak, U. Yapanel, B. Pel- lom, W. Ward, and P. Angkititrakul, “CU-MOVE: Advanced in- vehicle speech systems for route navigation,” in DSP for In- Vehicle and Mobile Systems. Springer-Verlag Publishing, 2004, ch. 2. 1280 Cited by 1 – Related articles – All 8 versions
A new perceptually motivated MVDR-based acoustic front-end (PMVDR) for robust automatic speech recognition [PDF] from nsysu.edu.tw UH Yapanel… – Speech Communication, 2008 – Elsevier … Moreover, MFCCs are quite fragile in noise, and additional compensation, such as feature enhancement and model adaptation, is needed for acceptable performance in realistic environments ([Hansen et al., 2001b], [Yapanel et al., 2002] and [CU-Move, 2004]). … Cited by 25 – Related articles – All 4 versions
[CITATION] Acoustic hole filling for sparse enrollment data using a cohort universal corpus for speaker recognition JW Suh… – The Journal of the Acoustical …, 2012 – Acoustical Society of America All 3 versions
[PDF] Two New Corpora for Audio-Visual Speech Processing [PDF] from psu.edu K Saenko, C La, B Zacka, J Glass, TJ Hazen… – 2008 – Citeseer … [1] JHL Hansen. In-Vehicle Speech Systems: ‘CU-Move’. DARPA Communicator Workshop, 2000. [2] Intel. AVCSR Toolkit. Available at http://sourceforge.net/projects/opencvlibrary/. [3] G. Potamianos and C. Neti. Audio-Visual Speech Recognition in Challenging Environments. … Related articles – View as HTML – All 4 versions
Towards an intelligent acoustic front end for automatic speech recognition: built-in speaker normalization [PDF] from hindawi.com UH Yapanel… – EURASIP Journal on Audio, Speech, and …, 2008 – dl.acm.org … We extracted the PMVDR features for the CU-Move in-vehicle speech [26] training set (see Section 6) (1) with no perceptual warping, (2) using the Bark scale (a = 0.57), and (3) using the BISN warp factors (see Section 5). Afterwards, we computed the variation of the trace … Related articles – All 15 versions
A speech presence microphone array beamformer using model based speech presence probability estimation T Yu… – Acoustics, Speech and Signal Processing, …, 2009 – ieeexplore.ieee.org Page 1. A SPEECH PRESENCE MICROPHONE ARRAY BEAMFORMER USING MODEL BASED SPEECH PRESENCE PROBABILITY ESTIMATION Tao Yu, John HL Hansen CRSS: Center of Robust Speech Systems, University … Cited by 2 – Related articles – All 3 versions
In-Set/Out-of-Set speaker recognition under sparse enrollment V Prakash… – Audio, Speech, and Language …, 2007 – ieeexplore.ieee.org … Experiments are performed using the following three separate corpora: 1) Noise-free TIMIT; 2) Noisy in-vehicle CU-Move; and 3) the NIST-SRE-2006 database. … Fig. 4 shows the corresponding plot for the CU-Move database from in-vehicle recordings. From Figs. … Cited by 10 – Related articles – BL Direct – All 6 versions
Model-based simulation of driver expectation in mountainous road using various control strategies W Wang, W Guo, Y Mao, X Jiang… – International Journal …, 2011 – Taylor & Francis … In environment, path condition the plane cur vehicle traje Meanwhile, i curve, speed driver expect desired traje between the t Fig. The star driver’s desir desired trajec the similar cu move the sta rotation range with the actua Experience o is familiar wi …
Detection of speech under physical stress: Model development, sensor selection, and feature fusion SA Patil… – 2008 – works.bepress.com … Previous research in this field has concentrated on stress classification, stress detection using the SUSAS corpus while more recently on other realistic conversational corpora including CU-Move (in-vehicle route navigation dialog), SOM (Soldier of the Month), FLETC corpus … Cited by 6 – Related articles – All 2 versions
Getting start with UTDrive: driver-behavior modeling and assessment of distraction for in-vehicle speech systems [PDF] from pitt.edu P Angkititrakul, DG Kwak, SJ Choi… – … Annual Conference of …, 2007 – isca-speech.org … vol. 20(2), pp. 151-170, Nov. 1996. [7] JHL Hansen, J. Plucienkowski, S. Gallant, R. Gallant, B. Pellom, and W. Ward, “CU-Move: Robust speech processing for in-vehicle speech sys- tems,” in ICSLP, pp. 524-527, 2000. [8] TB … Cited by 3 – Related articles – All 11 versions
[BOOK] DSP for In-vehicle and Mobile Systems H Abut, JHL Hansen… – 2005 – books.google.com … Chapter 2 presents the CU-Move in- vehicle corpus, and an overview of the CU-Move in-vehicle system that includes microphone array processing, environmental sniffing, speech features and robust recognition, and route dialog navigation information server. … Cited by 17 – Related articles – Library Search – All 8 versions
An FFT-based companding front end for noise-robust automatic speech recognition [PDF] from hindawi.com B Raj, L Turicchia, B Schmidt-Nielsen… – EURASIP Journal on …, 2007 – dl.acm.org … The optimal values of the width of the F and G filters and the degree of companding n were determined by experiments conducted on the CU-Move in-vehicle speech corpus [28] (the experimental setup is described in detail in Section 4). The lowest recognition error rates were … Cited by 7 – Related articles – All 16 versions
UTDrive: driver behavior and speech interactive systems for in-vehicle environments [PDF] from polito.it P Angkititrakul, M Petracca… – Intelligent Vehicles …, 2007 – ieeexplore.ieee.org … vol. 20(2), pp. 151-170, November 1996. [8] JHL Hansen, J. Plucienkowski, S. Gallant, R. Gallant, B. Pellom, and W. Ward, “CU-Move: Robust speech processing for in-vehicle speech systems,” in ICSLP, pp. 524-527, 2000. [9 … Cited by 16 – Related articles – All 7 versions
Speaker Source Localization Using Audio-Visual Data and Array Processing Based Speech Enhancement for In-Vehicle Environments X Zhang, JHL Hansen, K Takeda, T Maeno… – Advances for In-Vehicle …, 2007 – Springer … AUDIO-VISUAL DATA AND ARRAY PROCESSING BASED SPEECH ENHANCEMENT FOR IN-VEHICLE ENVIRONMENTS … In this chapter, we discuss safety with application for two interactive speech processing frameworks for in-vehicle systems. … Related articles – All 2 versions
A new perspective on feature extraction for robust in-vehicle speech recognition [PDF] from bltek.com UH Yapanel… – Eighth European Conference on …, 2003 – isca-speech.org … colorado.edu [21] Hansen, JHL, Angkititrakul P., Yapanel, U. et. al., “CU-Move: Analysis & Corpus Development for Interactive In-vehicle Speech Systems”, Proc. Eurospeech’01 [22] SPHERE software package, www.nist.gov [23 … Cited by 45 – Related articles – All 8 versions
Robust speech interaction in motorcycle environment [PDF] from upatras.gr I Mporas, O Kocsis, T Ganchev… – Expert Systems with …, 2010 – Elsevier … In the integration of speech-based interfaces within vehicle environments the research is conducted in two directions: (i) addition of … The CU-Move corpus consists of five domains, including digit strings, route navigation expressions, street and location sentences, phonetically … Cited by 4 – Related articles – All 4 versions
[PDF] A companding front end for noise-robust automatic speech recognition [PDF] from merl.com J Guinness, B Raj, B Schmidt-Nielsen… – Proceedings of IEEE …, 2005 – merl.com … We evaluated the companding front end on the digits component of the CU-Move in-vehicle speech corpus [8]. CU- Move consists of speech recorded in a car driving around various locations of the continental United States, under varying traffic and noise conditions. … Cited by 5 – Related articles – View as HTML – All 14 versions
CSA-BF: novel constrained switched adaptive beamforming for speech enhancement & recognition in real car environments X Zhang… – Acoustics, Speech, and Signal …, 2003 – ieeexplore.ieee.org … We demonstrated that the proposed CSA-BF processor can improve voice communications quality as reflected in a +5.5dB increase in SEGSNR, and speech recognition performace improvement by decreasing WER by 26-30.6% using CU-Move in-vehicle speech data. … Cited by 13 – Related articles – BL Direct – All 9 versions
Context-adaptive pre-processing scheme for robust speech recognition in fast-varying noise environment I Mporas, T Ganchev, O Kocsis… – Signal Processing, 2011 – Elsevier … The CU-Move corpus consists of five domains, including digit strings, route navigation expressions, street and location sentences, phonetically balanced sentences and a route navigation dialog in a human Wizard-of-Oz like scenario, considering a total of 500 speakers from the … Cited by 1 – Related articles – All 4 versions
[PDF] CFA-BF: a novel combined fixed/adaptive beamforming for robust speech recognition in real car environments [PDF] from cmu.edu X Zhang… – Interspeech/Eurospeech, 2003 – speech.cs.cmu.edu … moving car environments. We demonstrated that the CFA-BF can improve SEGSNR slightly, and improve speech recognition performance by decreasing WER by 29.3% using CU-Move in-vehicle speech data. We have shown … Cited by 11 – Related articles – All 5 versions
[PDF] UT-SCOPE-A corpus for Speech under Cognitive/Physical task Stress and Emotion [PDF] from limsi.fr V Varadarajan, JHL Hansen… – … for Research on Emotion and Affect …, 2006 – limsi.fr … 9. JHL Hansen, XX Zhang, M. Akbacak, UH. Yapanel, B. Pellom, W. Ward, P. Angkititrakul,” CU-MOVE: Advanced In-Vehicle Speech Systems for Route Navigation,” Chapter 2 in DSP for In-Vehicle and Mobile Systems, Springer-Verlag, 2004. 10. … Cited by 9 – Related articles – View as HTML – All 10 versions
Feature compensation in the cepstral domain employing model combination W Kim… – Speech Communication, 2009 – Elsevier … distance. The combined hybrid model, which consists of the selected Gaussian components is used for noisy speech model sharing. The performance is examined using Aurora2 and speech data for an in-vehicle environment. The … Cited by 19 – Related articles – All 4 versions
[PDF] TAICAR-the collection and annotation of an in-car speech database created in Taiwan [PDF] from aclweb.org HC Wang, CH Yang, JF Wang, CH Wu… – International Journal of …, 2005 – aclweb.org … The system was built in a Data Collection Vehicle (DCV) supporting the synchronous recording of multi-channel audio and video data through microphones and cameras. … Corpus name (year) CSDC- MoTiV (1998) SpeechDat- Car (1999) CU-Move (2000) CIAIR- HCC (2001) … Cited by 6 – Related articles – View as HTML – All 10 versions
Environmental sniffing: noise knowledge estimation for robust speech systems M Akbacak… – Acoustics, Speech, and Signal …, 2003 – ieeexplore.ieee.org … A microphone array and 8-channel digital recarder previously used for CU-Move in-vehicle speech data collection were employed [3]. The database does not contain speech. Fifteen noise classes are uanscribed during the data collection by a transcriber sitting in the car. … Cited by 26 – Related articles – BL Direct – All 10 versions
The SmartKom mobile car prototype system for flexible human-machine communication D Bühler… – Spoken multimodal human-computer dialogue in …, 2005 – Springer … Hansen, JH, Zhang, X., Akbacak, M., Yapanel, U., Pellom, B., and Ward, W. (2003). CU-Move: Advances in in-vehicle speech systems for route nav- igation. In Proceedings of IEEE Workshop in DSP in Mobile and Vehicular Systems, pages 1-6, Nagoya, Japan. … Cited by 3 – Related articles – All 3 versions
[BOOK] Social and emotional characteristics of speech-based in-vehicle information systems: impact on attitute and driving behaviour [PDF] from diva-portal.org IM Jonsson – 2009 – liu.diva-portal.org … of Speech-based In-Vehicle Information Systems: … Ing-Marie Jonsson Social and Emotional Characteristics of Speech-based In-Vehicle Information Systems: Impact on Attitude and Driving Behaviour Upplaga 1:1 ISBN 978-91-7393-478-7 ISSN 0282-9800 … Cited by 4 – Related articles – View as HTML – All 3 versions
Speaker verification using Gaussian mixture models within changing real car environments X Zhang, JHL Hansen, P Angkititrakul… – … Conference on Speech …, 2005 – isca-speech.org … 4. PERFORMANCE EVALUATION 5.1 CU-Move and CIAIR-HCC Databases Two in-vehicle corpora are used for our study: the CU-Move database [9] and CIAIR-HCC [10] database. Both corpora were collected in real car environments. … Related articles – All 2 versions
AVICAR: Audio-visual speech corpus in a car environment [PDF] from psu.edu B Lee, M Hasegawa-Johnson… – … on Spoken Language …, 2004 – isca-speech.org … pp. 2279-2282, 1999. [13] JHL Hansen, J. Plucienkowski, S. Gallant, B. Pellom, and W. Ward, “CU-Move: Robust speech processing for in-vehicle speech systems,” Proc. Int. Conf. Spoken Lang. Process., pp. 524-527, 2000. … Cited by 81 – Related articles – All 24 versions
CSA-BF: A constrained switched adaptive beamformer for speech enhancement and recognition in real car environments X Zhang… – Speech and Audio Processing, IEEE …, 2003 – ieeexplore.ieee.org … This paper is organized as follows. In Section II, we introduce the CU-Move in-vehicle speech database collected for devel- opment of in-vehicle route navigation. … II. CU-MOVE: IN-VEHICLE SPEECH CORPUS FOR INTERACTIVE SPEECH SYSTEMS … Cited by 20 – Related articles – BL Direct – All 5 versions
[PDF] Multi-dimensional data acquisition for integrated acoustic information research [PDF] from lrec-conf.org N Kawaguchi, S Matsubara, K Takeda… – Proc. of 3rd International …, 2002 – lrec-conf.org … “CU-Move” [7] is an in-vehicle speech dialogue system for route navigation and planning, which developed in Colorado University. They also perform a two-phase corpus development. First phase is for a noise Figure 4: Microphone Amplifier … Cited by 25 – Related articles – View as HTML – All 4 versions
High performance digit recognition in real car environments U Yapanel, X Zhang… – … International Conference on …, 2002 – isca-speech.org … of Colo., March 2001. [2] JHL Hansen, P. Angkititrakul, J. Plucienkowski, S. Gallant, U. Yapanel, B. Pellom, W. Ward, R. Cole, “CU-Move: Analysis & Corpus Development for Interactive In-vehicle Speech Systems”, Eurospeech’01 [3] http://cumove.colorado.edu [4] S. Riis, O … Cited by 14 – Related articles – All 2 versions
Advances in unsupervised audio classification and segmentation for the broadcast news and NGSW corpora R Huang… – Audio, Speech, and Language …, 2006 – ieeexplore.ieee.org … A set of five female and five male audio files are constructed for each noise type. A total of 14 noise types are considered, which come from the CU-move in-vehicle database [13] and an earlier speech recognition study [14]. … Cited by 36 – Related articles – All 6 versions
Environmental sniffing: Noise knowledge estimation for robust speech systems M Akbacak… – Audio, Speech, and Language …, 2007 – ieeexplore.ieee.org … sniffing framework, we collected in-vehicle acoustic noise data [ie, using a General Motors (GM) Blazer Sports Utility Vehicle (SUV)] using … a constructed five-channel microphone array, AKG microphone along the window frame in a manner similar to the CU-Move data collection … Cited by 26 – Related articles – BL Direct – All 4 versions
Czech audio-visual speech corpus of a car driver for in-vehicle audio-visual speech recognition [PDF] from enst.fr M Zelezný… – AVSP 2003-International Conference on …, 2003 – isca-speech.org … 8. References [1] JHL Hansen, P. Angkititrakul, S. Gallant, U. Yapanel, B. Pellom, W. Ward, and R. Cole, “Cu-move: Analysis & corpus development for interactive in-vehicle speech sys- tems,” in Proceedings of Eurospeech 2001 (CD-ROM), °Alborg, Denmark, 2001. … Cited by 5 – Related articles – All 5 versions
Environmental sniffing: robust digit recognition for an in-vehicle environment [PDF] from cmu.edu M Akbacak… – Eighth European Conference on …, 2003 – isca-speech.org … [4] JHL Hansen, et al., “CU-Move: Analysis & Corpus Development for Interactive In-Vehicle Speech Systems,” Eurospeech, v3, 2023-6, 2001. [5] B. Zhou, JHL Hansen, “Unsupervised Audio Stream Segmentation and Clustering via the Bayesian Information Criterion,” Proc. … Cited by 9 – Related articles – All 4 versions
Blindsight: eyes-free access to mobile phones [PDF] from psu.edu KA Li, P Baudisch… – Proceedings of the twenty-sixth annual …, 2008 – dl.acm.org Page 1. BlindSight: Eyes-Free Access to Mobile Phones Kevin A. Li Computer Science and Engineering University of California, San Diego k2li@cs.ucsd.edu Patrick Baudisch, Ken Hinckley Microsoft Research Redmond, WA {baudisch, kenh}@microsoft.com … Cited by 43 – Related articles – BL Direct – All 19 versions
Construction and Analysis of a Multi-Layered In-car Spoken Dialogue Corpus N Kawaguchi, S Matsubara, I Kishida, Y Irie… – DSP for in-vehicle and …, 2005 – Springer … 1‚ pp. 633-639‚ Aug. 2002‚ Taipei. J. Hansen‚ P. Angkititrakul‚ J. Plucienkowski‚ S.Gallant‚ U. Yapanel‚ B. Pellom‚ W. Ward‚ and R. Cole: “CU-Move”: Analysis & Corpus Development for Interactive In-Vehicle Speech Systems‚ Proc. … Cited by 14 – Related articles – All 4 versions
Speech under stress: Analysis, modeling and recognition SA Patil… – 2007 – works.bepress.com … More recently, we have considered other realistic conversational corpora including CU-Move (in-vehicle route navigation dialog), FLETC corpus (police/military training scenario), and UT-SCOPE (speech under cognitive and physical stress conditions) [20,23,26]. … Cited by 31 – Related articles – BL Direct – All 5 versions
[PDF] Speech-Based Interfaces in Vehicles [PDF] from tytlabs.co.jp T Wakita – Conference on Spoken Language Processing, 2002 – tytlabs.co.jp … “CU-Move : Analysis & Corpus Development for Interactive In-Vehicle Speech Systems,” Eurospeech-2001, (2001), 2023 4) Hirsh, HG and Pearce, D. : “The AURORA Experimental Framework for the Performance Evaluations of Speech Recognition Systems under Noisy … Cited by 4 – Related articles – All 3 versions
Application of Speech Technology in Vehicles F Chen, IM Jonsson, J Villing… – Speech Technology, 2010 – Springer … CU-move is a DARPA (Defense Advanced Research Projects Agency) project with the goal of developing algorithms and technology for robust access … The main difference between dialogue system designs for in-vehicle use, compared to other environments, is the fact that the … Cited by 1 – Related articles – All 2 versions
In-vehicle acoustic chamber ARMA modeling and classification S Kadambe – Digital Signal Processing Workshop, 2002 and …, 2002 – ieeexplore.ieee.org … The results of these two studies will be reported in the future paper. REFERENCES 1. J. H. L. Hansen, J. Plucienkowski, S. Gallant, B. Pellom and W. Ward, “”CU-Move”: Robust speech processing for in- vehicle speech systems,” Proc. Of ICSLP 2000, vol. I, pp. …
An advanced Japanese speech corpus for in-car spoken dialogue research [PDF] from nagoya-u.ac.jp Y Irie, N Kawaguchi, S Matsubara, I Kishida… – 2003 – ir2.nul.nagoya-u.ac.jp … 1, pp. 633-639, Aug. 2002, Taipei. [8] J. Hansen, P. Angkititrakul, J. Plucienkowski, S.Gallant, U. Yapanel, B. Pellom, W. Ward, and R. Cole: “CU-Move”: Analysis & Corpus Development for Interactive In-Vehicle Speech Systems, Proc. … Cited by 7 – Related articles – All 3 versions
[BOOK] Columbia University off the record [HTML] from google.com A Millet – 2011 – books.google.com … Crimes on Campus Aggravated Assault: 5 Arson: 0 Burglary: 87 Murder/Manslaughter: 0 Robbery: 6 Sex Offenses: 5 Vehicle Theft: 0 … Health Promotion Program Contraception Counseling services Dental care options Disability services Exercise program: CU Move Go Ask Alice …
[PDF] Analysis of a large in-car speech corpus and its application to the multimodel ASR [PDF] from korea.ac.kr H Fujimura, C Miyajima, K Itou… – Proc. 2005 IEEE …, 2005 – ispl.korea.ac.kr … ICSLP2000, pp.IV378-IV381, 2000. [5] JHLHansen et al., “CU-Move: Robust Speech Processing for In-Vehicle Speech Systems,” Proc. ICSLP2000, pp.I527- 530, 2000. [6] A.Moreno et al., “SpeechDat-Car: A Large Speech Database for Automotive Environments,” Proc. … Cited by 2 – Related articles – View as HTML – All 7 versions
Intelligent dialog overcomes speech technology limitations: The SENECA example [PDF] from iuiconf.org W Minker, U Haiber, P Heisterkamp… – Proceedings of the 8th …, 2003 – dl.acm.org … Communication Systems. Detroit Auto Interior Show, 2001. 2. Hansen, JHL, Plucienkowski, J., Gallant, S., Pellom, BL, and Ward, W. CU-Move: Robust Speech Processing for In-Vehicle Speech Systems. ICSLP, 2000. 3. Heisterkamp … Cited by 13 – Related articles – All 10 versions
[PDF] Signal processing technology challenges of cognitive radio [PDF] from eet-china.com J Mitola III – Defense Advanced Research Projects Agency, 2003 – eet-china.com … 4 Biometric Consortium Research & Databases (www.biometrics.org) July 03 5 Center for Spoken Dialog Research projects NSF- ITR Reading Tutor Project,, DARPA Communicator Project, and CU Move In-Vehicle Research (cslr.colorado.edu), Jul 03 … Cited by 5 – Related articles – View as HTML – All 4 versions
[CITATION] An Analysis of the Distributed Architecture for a Telephone Based Human Computer Dialogue System ? H WANG… – Chinese Journal of Electronics, 2004 BL Direct
Use of Multiple Speech Recognition Units in an In-car Assistance System A Brutti, P Coletti, L Cristoforetti, P Geutner… – DSP for In-Vehicle and …, 2005 – Springer … 111 [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] JHL Hansen, X. Zhang, M. Akbacak, U. Yapanel, B. Pellom, W. Ward, “CU-Move: Advances in In-Vehicle Speech Systems for Route Navigation”, Proc. of the Workshop on DSP in Mobile and Vehicular Systems, Nagoya, Japan, 2003. … Related articles – All 2 versions
Towards an evaluation standard for speech control concepts in real-world scenarios [PDF] from cmu.edu J Maase, D Hirschfeld, U Koloska… – Eighth European …, 2003 – isca-speech.org … IEEE ICASSP, 1984, pp. 42.11.1-42.11.4. [2] Hansen, JHL, et. al. : “CU-Move : Analysis & Corpus development for Interactive In-vehicle Speech systems”, Proceedings of EUROSPEECH 2001, Aalborg, 2001. [3] Moreno, A., Lindberg, B., Draxler, C. et. al.: “SPEECHDAT-CAR. … Cited by 12 – Related articles – All 2 versions
A continuous speech recognition evaluation protocol for the AVICAR database [PDF] from qut.edu.au T Kleinschmidt, D Dean, S Sridharan… – 2007 – eprints.qut.edu.au … Conf. on Language Resources and Evaluation, 2000. [6] J. Hansen, J. Plucienkowski, S. Gallant, B. Pellom, and W. Ward, “Cu- move: Robust speech processing for in-vehicle speech systems,” in 6th Int. Conf. on Spoken Language Processing, vol. 1, Beijing, China, 2000, pp. … Cited by 10 – Related articles – All 8 versions
A Wizard of Oz Framework for Collecting Spoken Human-Computer Dialogs [PDF] from ed.ac.uk R Mishra, E Shriberg, S Upson, J Chen… – … on Spoken Language …, 2004 – isca-speech.org … In a driving environment, humans might choose to give navigation information differently with respect to the current position of the vehicle (eg, close to a turn) and external … The DARPA CU-Move project1 records speech in real driving environments using similar methods. … Cited by 1 – Related articles – All 6 versions
Intelligent dialogue strategy for accessing infotainment applications in mobile environments W Minker, U Haiber, P Heisterkamp… – ISCA Tutorial and …, 2002 – isca-speech.org … Hansen, J., Plucienkowski, J., Gallant, S., Pellom, B., and Ward, W. (2000). CU-Move: Robust Speech Processing for In-Vehicle Speech Systems. In ICSLP. Heisterkamp, P. (2001). Linguatronic: Product-Level Speech System for Mercedes-Benz Car. In HLT. … Cited by 5 – Related articles
A Novel Mask Estimation Method Employing Posterior-Based Representative Mean Estimate for Missing-Feature Speech Recognition W Kim… – Audio, Speech, and Language Processing, …, 2011 – ieeexplore.ieee.org Page 1. Copyright (c) 2010 IEEE. Personal use is permitted. For any other purposes, Permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. … Cited by 1 – Related articles – All 2 versions
Jointly Gaussian PDF-based likelihood ratio test for voice activity detection [PDF] from ugr.es JM Gorriz, J Ramírez, EW Lang… – Audio, Speech, and …, 2008 – ieeexplore.ieee.org … As an example, there is an increasing interest of studying the effect of adverse noise conditions on ASR systems in vehicle environment [13]. The in-vehicle environment pro- vides a rich background and the big amount of noise … Cited by 12 – Related articles – All 7 versions
The SENECA spoken language dialogue system W Minker, U Haiber, P Heisterkamp… – Speech communication, 2004 – Elsevier … 1. Introduction. People spend more time in their cars and want to make that time as enjoyable and productive as possible. Therefore, a large variety of electronic systems have been made available in the vehicle for comfort, ease of driving, entertainment, and communications. … Cited by 18 – Related articles – All 3 versions
Utterance Classification for Combination of Multiple Simple Dialog Systems [PDF] from tohoku.ac.jp SJ Hahm, A Ito, K Awano, M Ito… – Parallel and Distributed …, 2011 – ieeexplore.ieee.org … capability,” Proc. ICASSP, vol. I, pp. 433-436, 2004. [3] JHL Hansen, J. Plucienkowski, S. Gallant, B. Pellom and W. Ward, “”CU-move”: robust speech processing for in-vehicle speech systems,” Proc. ICSLP, vol.1, 524-527, 2000. [4] B … Related articles – All 4 versions
Construction and evaluation of a large in-car speech corpus [PDF] from ieice.org K Takeda, H Fujimura, I Katsunobu… – IEICE transactions on …, 2005 – search.ieice.org … [8] JHL Hansen, J. Plucienkowski, S. Gallant, B. Pellom, and W. Ward, “CU-Move: Robust speech processing for in-vehicle speech systems,” Proc. International Conference on Spoken Language Pro- cessing (ICSLP2000), pp.I527-I530, Beijing, 2000. … Cited by 15 – Related articles – BL Direct – All 13 versions
Remotely navigated mobile platform using voice command [PDF] from unimap.edu.my O Khairulnizam… – 2009 – dspace.unimap.edu.my … Technical Report UMTRI-2006-5, Feb. 2006. [18] JHL Hansen, J. Plucienkowski, S. Gallant, R. Gallant, B. Pellom, and W. Ward, .CU-Move: Robust speech processing for in-vehicle speech systems,. in ICSLP, pp. 524.527,2000. Related articles – All 4 versions
Design, implementation and evaluation of the SENECA spoken language dialogue system W Minker, U Haiber, P Heisterkamp… – … -Computer Dialogue in …, 2005 – Springer … 1. Introduction People spend more time in their cars and want to make that time as en- joyable and productive as possible. Therefore, a large variety of electronic systems have been made available in the vehicle for comfort, ease of driving, entertainment and communications. … Cited by 3 – Related articles – All 2 versions
[PDF] Incentivization of e-government [PDF] from governmentontheweb.org H Margetts… – … Delivery. For the UK National Audit …, 2003 – governmentontheweb.org Page 1. Incentivization of e-government Transforming the performance of HM Customs and Excise through Electronic Helen Margetts and Hala Yared, School of Public Policy, UCL, commissioned by the National Audit Office 1. Introduction 1.1 n government ns nothing unless … Cited by 7 – Related articles – View as HTML – All 11 versions
[PDF] State of the art in dialogue management [PDF] from ed.ac.uk R Catizone, A Setzer… – Deliverable D, 2002 – groups.inf.ed.ac.uk Page 1. Authors: R. Catizone, A. Setzer, Y. Wilks Date: September 2002 Deliverable D5.1 State of the Art in Dialogue Management Document History Version Editor Date Explanation Status 0.1 Catizone, Setzer, Wilks August 2002 Draft version to be read by the partners. … Cited by 4 – Related articles – View as HTML – All 5 versions
DESIGN, IMPLEMENTATION AND EVALUATION OF THE SENECA SPOKEN LANGUAGE DIALOGUE U Haiber, P Heisterkamp… – Spoken multimodal human- …, 2005 – books.google.com … 1. Introduction People spend more time in their cars and want to make that time as en- joyable and productive as possible. Therefore, a large variety of electronic systems have been made available in the vehicle for comfort, ease of driving, entertainment and communications. … Related articles – All 2 versions
Evaluation and usability of multimodal spoken language dialogue systems [PDF] from nislab.dk L Dybkjaer, NO Bernsen… – Speech Communication, 2004 – Elsevier … into speech and human-computer interaction, Reading Tutor project 6 that concerns intelligent animated computer characters capable of natural face-to-face conversational interaction in specific task domains), CU Move 7 which is a DARPA in-vehicle automatic speech … Cited by 62 – Related articles – All 8 versions
[PDF] A robust architecture for human language technology systems [PDF] from piconepress.com T Stanley – 2006 – isip.piconepress.com … Electronic Systems (IES) program and friends who have made my graduate studies memorable. I sincerely thank the Center for Advanced Vehicular System (CAVS) for funding and supporting the In-Vehicle Dialog Systems project. Page 8. iv TABLE OF CONTENTS Page … Related articles – View as HTML – Library Search – All 7 versions
Rapid discriminative acoustic model based on eigenspace mapping for fast speaker adaptation B Zhou… – Speech and Audio Processing, IEEE …, 2005 – ieeexplore.ieee.org Page 1. 554 IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 13, NO. 4, JULY 2005 Rapid Discriminative Acoustic Model Based on Eigenspace Mapping for Fast Speaker Adaptation Bowen Zhou, Member … Cited by 14 – Related articles – All 3 versions
[CITATION] On the Robustness of Real-world Spoken Dialogue System HC Wang – 2003 Related articles