Center for Robust Speech Systems

[1] K. Godin, S. O. Sadjadi, and J. H. Hansen, “Impact of noise reduction and spectrum estimation on noise robust speaker identification,” in Proc. INTERSPEECH, 2013. [ bib | .pdf ]
[2] M. Graciarena, A. Alwan, D. Ellis, H. Franco, L. Ferrer, J. H. Hansen, A. Janin, B.-S. Lee, Y. Lei, V. Mitra, N. Morgan, S. O. Sadjadi, T. Tsai, N. Scheffer, L. N. Tan, and B. Williams, “All for one: Feature combination for highly channel-degraded speech activity detection,” in Proc. INTERSPEECH, 2013. [ bib | .pdf ]
[3] T. Hasan, S. O. Sadjadi, G. Liu, N. Shokouhi, H. Bořil, and J. H. Hansen, “CRSS systems for 2012 NIST speaker recognition evaluation,” in Proc. IEEE ICASSP, 2013. [ bib | .pdf ]
[4] S. O. Sadjadi and J. H. Hansen, “Unsupervised speech activity detection using voicing measures and perceptual spectral flux,” IEEE Signal Processing Letters, vol. 20, pp. 197-200, 2013. [ bib | .pdf ]
[5] S. O. Sadjadi and J. H. Hansen, “Robust front-end processing for speaker identification over extremely degraded communication channels,” in Proc. IEEE ICASSP, 2013. [ bib | .pdf ]
[6] H. Bořil, A. Sangwan, and J. H. L. Hansen, “Arabic dialect identification - 'is the secret in the silence?' and other observations,” in Proc. Interspeech, 2012. [ bib | .pdf ]
[7] A. Das and J. H. L. Hansen, “Constrained iterative speech enhancement using phonetic classes,” IEEE Trans. Audio Speech Lang. Process., vol. 20, pp. 1869-1883, 2012. [ bib | .pdf ]
[8] K. W. Godin, T. Hasan, and J. H. L. Hansen, “Glottal waveform analysis of physical task stress speech,” in Proc. Interspeech, 2012. [ bib ]
[9] T. Hasan and J. H. L. Hansen, “Factor analysis of acoustic features using a mixture of probabilistic principal component analyzers for robust speaker verification,” in Proc. Odyssey, 2012. [ bib | .pdf ]
[10] T. Hasan and J. H. L. Hansen, “Front-end channel compensation using mixture-dependent feature transformations for i-vector speaker recognition,” in Proc. Interspeech, 2012. [ bib | .pdf ]
[11] T. Hasan and J. H. L. Hansen, “Integrated feature normalization and enhancement for robust speaker recognition using acoustic factor analysis,” in Proc. Interspeech, 2012. [ bib | .pdf ]
[12] G. Liu, J.-W. Suh, and J. H. Hansen, “A fast speaker verification with universal background support data selection,” in Proc. IEEE ICASSP, 2012. [ bib | .pdf ]
[13] G. Liu, C. Zhang, and J. H. Hansen, “A linguistic data acquisition front-end for language recognition evaluation,” in Proc. Odyssey, 2012. [ bib | .pdf ]
[14] S. O. Sadjadi and J. H. Hansen, “Blind reverberation mitigation for robust speaker identification,” in Proc. IEEE ICASSP, 2012. [ bib | .pdf ]
[15] S. O. Sadjadi, T. Hasan, and J. H. Hansen, “Mean hilbert envelope coefficients (MHEC) for robust speaker recognition,” in Proc. Interspeech, 2012. [ bib | .pdf ]
[16] K. W. Godin and J. H. L. Hansen, “Vowel context and speaker interactions influencing glottal open quotient and formant frequency shifts in physical task stress,” in Proc. Interspeech, pp. 2945-2948, 2011. [ bib | .pdf ]
[17] K. W. Godin and J. H. L. Hansen, “Analysis of the effects of physical task stress on the speech signal,” J. Acoust. Soc. Am., vol. 130, pp. 3992-3998, 2011. [ bib | .pdf ]
[18] S. O. Sadjadi and J. H. Hansen, “Hilbert envelope based features for robust speaker identification under reverberant mismatched conditions,” in Proc. IEEE ICASSP, 2011. [ bib | .pdf ]
[19] H. Bořil and J. H. L. Hansen, “Unsupervised equalization of lombard effect for speech recognition in noisy adverse environments,” IEEE Trans. Audio Speech Lang. Process., vol. 18, pp. 1379-1393, Aug. 2010. [ bib | .pdf ]
[20] K. W. Godin and J. H. L. Hansen, “Session variability contrasts in the MARP corpus,” in Proc. Interspeech, pp. 298-301, 2010. [ bib | .pdf ]
[21] T. Hasan, Y. Lei, A. Chandrasekaran, and J. H. L. Hansen, “A novel feature sub-sampling method for efficient universal background model training in speaker verification,” in Proc. IEEE ICASSP, pp. 4494-4497, 2010. [ bib | .pdf ]
[22] Y. Lei and J. H. Hansen, “Speaker recognition using supervised probabilistic principal component analysis,” in Proc. Interspeech, pp. 382-385, 2010. [ bib | .pdf ]
[23] Y. Lei, T. Hasan, J.-W. Suh, A. Sangwan, H. Boril, L. Gang, K. Godin, and J. H. L. Hansen, “The crss systems for the 2010 nist speaker recognition evaluation,” in Proc. NIST SRE, 2010. [ bib | .pdf ]
[24] G. Liu, Y. Lei, and J. H. L. Hansen, “A novel feature extraction strategy for multi-stream robust emotion identification,” in Proc. Interspeech, pp. 482-485, 2010. [ bib | .pdf ]
[25] S. A. Patil and J. H. L. Hansen, “The physiological microphone (PMIC): A competitive alternative for speaker assessment in stress detection and speaker verification,” Speech Commun., vol. 52, pp. 327-340, 2010. [ bib | .pdf ]
[26] S. A. Patil, A. Sangwan, and J. H. L. Hansen, “Speech under physical stress: A production-based framework,” in Proc. IEEE ICASSP, pp. 5146-5149, 2010. [ bib | .pdf ]
[27] S. O. Sadjadi and J. H. L. Hansen, “Assessment of single-channel speech enhancement techniques for speaker identification under mismatched conditions,” in Proc. Interspeech, pp. 2138-2141, 2010. [ bib | .pdf ]
[28] H. Bořil and J. H. L. Hansen, “Unsupervised equalization of lombard effect for speech recognition in noisy adverse environment,” in Proc. IEEE ICASSP, 2009. [ bib | .pdf ]
[29] X. Fan and J. H. L. Hansen, “Speaker identification with whispered speceh based on modified LFCC parameters and feature mapping,” in Proc. IEEE ICASSP, 2009. [ bib | .pdf ]
[30] J. H. L. Hansen and V. Varadarajan, “Analysis and compensation of lombard speech across noise type and levels with application to in-set/out-of-set speaker recognition,” IEEE Trans. Audio Speech Lang. Process., vol. 17, pp. 366-378, 2009. [ bib | .pdf ]
[31] W. Kim and J. H. L. Hansen, “Robust angry speech detection employing a teo-based discriminative classifier combination,” in Proc. Interspeech, pp. 2019-2022, 2009. [ bib | .pdf ]
[32] Y. Lei and J. H. L. Hansen, “The role of age in factor analysis for speaker identification,” in Proc. Interspeech, 2009. [ bib | .pdf ]
[33] K. W. Godin and J. H. L. Hansen, “Analysis and perception of speech under physical task stress,” in Proc. Interspeech, (Brisbane, Australia), pp. 1674-1677, Sep. 2008. [ bib | .pdf ]
[34] S. A. Patil and J. H. L. Hansen, “Detection of speech under physical stress: Model development, sensor selection, and feature fusion,” in Proc. Interspeech, 2008. [ bib | .pdf ]
[35] U. H. Yapanel and J. H. Hansen, “A new perceptually motivated mvdr-based acoustic front-end (pmvdr) for robust automatic speech recognition,” Speech Commun., vol. 50, pp. 142-152, 2008. [ bib | .pdf ]
[36] A. Ikeno, V. Varadarajan, S. Patil, and J. H. L. Hansen, “UT-Scope: Speech under lombard effect and cognitive stress,” in Proc. IEEE Aerospace Conf., (Big Sky, Montana), pp. 1-7, 2007. [ bib | .pdf ]
[37] V. Varadarajan and J. H. L. Hansen, “Analysis of lombard effect under different types and levels of noise with application to in-set speaker id systems,” in Proc. Interspeech, pp. 937-940, ISCA, September 2006. [ bib | .pdf ]
[38] R. Huang and J. H. L. Hansen, “Advances in unsupervised audio classification and segmentation for the broadcast news and ngsw corpora,” IEEE Trans. Audio Speech Lang. Process., vol. 14, pp. 907-919, May 2006. [ bib | .pdf ]
[39] V. Varadarajan, J. H. L. Hansen, and A. Ikeno, “UT-Scope - a corpus for speech under cognitive/physical task stress and emotion,” in Proc. LREC Workshop Speech Under Emotion, May 2006. [ bib ]
[40] E. Ruzanski, J. H. L. Hansen, J. Meyerhoff, G. Saviolakis, and M. Koenig, “Effects of phoneme characteristics on teo feature-based automatic stress detection in speech,” in Proc. IEEE ICASSP, 2005. [ bib | .pdf ]
[41] R. Huang and J. H. L. Hansen, “Advances in unsupervised audio segmentation for the broadcast news and ngsw corpora,” in Proc. IEEE ICASSP, vol. 1, pp. I-741-4vol.1, 17-21 May 2004. [ bib | DOI | .pdf ]
[42] U. Yapanel and J. H. L. Hansen, “A new perspective on feature extraction for robust in-vehicle speech recognition,” in Proc. Eurospeech, 2003. [ bib | .pdf ]
[43] M. A. Rahurkar, J. H. Hansen, J. Meyerhoff, G. Saviolakis, and M. Koenig, “Frequency band analysis for stress detection using a teager energy operator based feature,” in Proc. ICSLP, pp. 2021-2024, 2002. [ bib | .pdf ]
[44] G. Zhou, J. H. L. Hansen, and J. F. Kaiser, “Nonlinear feature based classification of speech under stress,” IEEE Trans. Speech Audio Process., vol. 9, pp. 201-216, March 2001. [ bib | .pdf ]
[45] J. Jensen and J. H. L. Hansen, “Speech enhancement using a constrained iterative sinusoidal model,” IEEE Trans. Speech Audio Process., vol. 9, pp. 731-740, 2001. [ bib | .pdf ]
[46] S. E. Bou-Ghazale and J. H. L. Hansen, “A comparative study of traditional and newly proposed features for recognition of speech under stress,” IEEE Trans. Speech Audio Process., vol. 8, pp. 429-442, July 2000. [ bib | .pdf ]
[47] C. Vloeberghs, P. Verlinde, C. Swail, H. Steeneken, D. van Leeuwen, I. Trancoso, A. South, R. Moore, E. J. Cupples, T. Anderson, and J. Hansen, “The impact of speech under stress on military speech technology,” Tech. Rep. RTO-TR-10, NATO Research and Technology Organization, March 2000. [ bib | .pdf ]
[48] J. R. Deller, J. H. L. Hansen, and J. G. Proakis, Discrete-Time Processing of Speech Signals. IEEE Press, Piscataway, NJ, 2000. [ bib ]
[49] B. D. Womack and J. H. L. Hansen, “N-channel hidden markov models for combined stressed speech classification and recognition,” IEEE Trans. Speech Audio Process., vol. 7, pp. 668-677, November 1999. [ bib | .pdf ]
[50] B. L. Pellom and J. H. L. Hansen, “An experimental study of speaker verification sensitivity to computer voice-altered imposters,” in Proc. IEEE ICASSP, 1999. [ bib | .pdf ]
[51] B. L. Pellom and J. H. L. Hansen, “An improved (Auto:I, LSP:T) constrained iterative speech enhancement for colored noise environments,” IEEE Trans. Speech Audio Process., vol. 6, no. 6, pp. 573-579, 1998. [ bib | .pdf ]
[52] R. Sarikaya, B. L. Pellom, and J. H. L. Hansen, “Wavelet packet transfrom features with application to speaker identification,” in Nordic Signal Processing Symposium, 1998. [ bib | .pdf ]
[53] G. Zhou, J. H. L. Hansen, and J. F. Kaiser, “Linear and nonlinear speech feature analysis for stress classification,” in Proc. ICSLP, 1998. [ bib | .pdf ]
[54] B. D. Womack and J. H. L. Hansen, “Classification of speech under stress using target driven features,” Speech Commun., vol. 20, pp. 131-150, November 1996. [ bib | .pdf ]
[55] J. H. L. Hansen and B. D. Womack, “Feature analysis and neural network-based classification of speech under stress,” IEEE Trans. Speech Audio Process., vol. 4, pp. 307-313, July 1996. [ bib | .pdf ]
[56] J. H. L. Hansen, “Analysis and compensation of speech under stress and noise for environmental robustness in speech recognition,” Speech Commun., vol. 20, pp. 151-173, Nov. 1996. [ bib | .pdf ]
[57] B. D. Womack and J. H. L. Hansen, “Improved speech recognition via speaker stress directed classification,” in Proc. IEEE ICASSP, (Atlanta), pp. 53-57, 1996. [ bib | .pdf ]
[58] B. D. Womack and J. H. L. Hansen, “Stress independent robust HMM speech recognition using neural network stress classification,” in Proc. Eurospeech, September 1995. [ bib | .pdf ]
[59] S. E. Bou-Ghazale and J. H. L. Hansen, “A source generator based modeling framework for synthesis of speech under stress,” in Proc. IEEE ICASSP, 1995. [ bib | .pdf ]
[60] J. H. L. Hansen and L. M. Arslan, “Robust feature-estimation and objective quality assessment for noisy speech recognition using the credit card corpus,” IEEE Trans. Speech Audio Process., vol. 3, no. 3, pp. 169-184, 1995. [ bib | .pdf ]
[61] J. H. L. Hansen and S. Nandkumar, “Robust estimation of speech in noisy backgrouds based on aspects of the auditory process,” J. Acoust. Soc. Am., vol. 97, no. 6, pp. 3833-3849, 1995. [ bib | .pdf ]
[62] D. A. Cairns and J. H. L. Hansen, “Nonlinear analysis and classification of speech under stressed conditions,” J. Acoust. Soc. Am., vol. 96, pp. 3392-3400, December 1994. [ bib | .pdf ]
[63] S. E. Bou-Ghazale and J. H. L. Hansen, “Duration and spectral based token generation for HMM speech recognition under stress,” in Proc. IEEE ICASSP, vol. i, pp. 413-416, 1994. [ bib | .pdf ]
[64] J. H. L. Hansen, “Morphological constrained feature enhancement with adaptive cepstral compensation (mce-acc) for speech recognition in noise and lombard effect,” IEEE Trans. Speech Audio Proc., vol. 2, no. 4, pp. 598-614, 1994. [ bib | .pdf ]
[65] J. H. L. Hansen and M. A. Clements, “Constrained iterative speech enhancement with application to speech recognition,” IEEE Trans. Signal Process., vol. 39, no. 4, pp. 795-805, 1991. [ bib | .pdf ]
[66] J. H. L. Hansen, “Evaluation of acoustic correlates of speech under stress for robust speech recognition,” in Proc. Fifteenth Annual Northeast Bioengineering Conf., (Boston), pp. 31-32, March 1989. [ bib | .pdf ]
[67] J. H. L. Hansen and M. A. Clements, “Stress compensation and noise reduction algorithms for robust speech recognition,” in Proc. IEEE ICASSP, pp. 266-269, 1989. [ bib | .pdf ]
[68] J. H. L. Hansen, Analysis and compensation of stressed and noisy speech with application to robust automatic recognition. PhD thesis, Georgia Inst. Tech., Atlanta, GA, July 1988. [ bib | .pdf ]
[69] J. H. L. Hansen and M. A. Clements, “Constrained iterative speech enhancement with application to automatic speech recognition,” in Proc. IEEE ICASSP, pp. 561-564, 1988. [ bib | .pdf ]
[70] J. H. L. Hansen and M. A. Clements, “Iterative speech enhancement with spectral constraints,” in Proc. IEEE ICASSP, pp. 189-192, 1987. [ bib | .pdf ]

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