@conference {14229,
	title = {Measuring 1st order stretchwith a single filter},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008},
	year = {2008},
	month = {2008/04/31/March},
	pages = {909 - 912},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We analytically develop a filter that is able to measure the linear stretch of the transformation around a point, and present results of applying it to real signals. We show that this method is a real-time alternative solution for measuring local signal transformations. Experimentally, this method can accurately measure stretch, however, it is sensitive to shift.},
	keywords = {Cepstral analysis, Educational institutions, filter, filtering theory, Fourier transforms, Frequency domain analysis, Frequency estimation, Gabor filters, Image analysis, IMAGE PROCESSING, linear stretch measurement, local signal transformation measurement, Nonlinear filters, Phase estimation, Signal analysis, Speech processing},
	isbn = {978-1-4244-1483-3},
	doi = {10.1109/ICASSP.2008.4517758},
	author = {Bitsakos,K. and Domke, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {14207,
	title = {Combining motion from texture and lines for visual navigation},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. IROS 2007},
	year = {2007},
	month = {2007/11/29/Oct. },
	pages = {232 - 239},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Two novel methods for computing 3D structure information from video for a piecewise planar scene are presented. The first method is based on a new line constraint, which clearly separates the estimation of distance from the estimation of slant. The second method exploits the concepts of phase correlation to compute from the change of image frequencies of a textured plane, distance and slant information. The two different estimates together with structure estimates from classical image motion are combined and integrated over time using an extended Kalman filter. The estimation of the scene structure is demonstrated experimentally in a motion control algorithm that allows the robot to move along a corridor. We demonstrate the efficacy of each individual method and their combination and show that the method allows for visual navigation in textured as well as un-textured environments.},
	keywords = {3D structure information, CAMERAS, Computer vision, extended Kalman filter, Frequency, image frequencies, Image motion analysis, Image texture, Kalman filters, Layout, motion control, Motion estimation, Navigation, Optical computing, phase correlation, piecewise planar scene, Robustness, Simultaneous localization and mapping, Speech processing, textured plane, video signal processing, visual navigation},
	isbn = {978-1-4244-0912-9},
	doi = {10.1109/IROS.2007.4399568},
	author = {Bitsakos,K. and Li Yi and Ferm{\"u}ller, Cornelia}
}
@conference {13849,
	title = {Reranking for Sentence Boundary Detection in Conversational Speech},
	booktitle = {2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings},
	volume = {1},
	year = {2006},
	month = {2006/05/14/19},
	pages = {I-I - I-I},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a reranking approach to sentence-like unit (SU) boundary detection, one of the EARS metadata extraction tasks. Techniques for generating relatively small n-best lists with high oracle accuracy are presented. For each candidate, features are derived from a range of information sources, including the output of a number of parsers. Our approach yields significant improvements over the best performing system from the NIST RT-04F community evaluation},
	keywords = {Automatic speech recognition, conversational speech, data mining, Ear, EARS metadata extraction tasks, Feature extraction, hidden Markov models, meta data, Model driven engineering, NIST, NIST RT-04F community evaluation, oracle accuracy, performance evaluation, reranking, sentence-like unit boundary detection, Speech processing, Speech recognition, Telephony},
	isbn = {1-4244-0469-X},
	doi = {10.1109/ICASSP.2006.1660078},
	author = {Roark,B. and Liu,Yang and Harper,M. and Stewart,R. and Lease,M. and Snover,M. and Shafran,I. and Dorr, Bonnie J and Hale,J. and Krasnyanskaya,A. and Yung,L.}
}
@article {18462,
	title = {Processing of reverberant speech for time-delay estimation},
	journal = {IEEE Transactions on Speech and Audio Processing},
	volume = {13},
	year = {2005},
	month = {2005/11//},
	pages = {1110 - 1118},
	abstract = {In this paper, we present a method of extracting the time-delay between speech signals collected at two microphone locations. Time-delay estimation from microphone outputs is the first step for many sound localization algorithms, and also for enhancement of speech. For time-delay estimation, speech signals are normally processed using short-time spectral information (either magnitude or phase or both). The spectral features are affected by degradations in speech caused by noise and reverberation. Features corresponding to the excitation source of the speech production mechanism are robust to such degradations. We show that these source features can be extracted reliably from the speech signal. The time-delay estimate can be obtained using the features extracted even from short segments (50-100 ms) of speech from a pair of microphones. The proposed method for time-delay estimation is found to perform better than the generalized cross-correlation (GCC) approach. A method for enhancement of speech is also proposed using the knowledge of the time-delay and the information of the excitation source.},
	keywords = {Acoustic noise, acoustic signal processing, array signal processing, data mining, Degradation, delay estimation, Feature extraction, Hilbert envelope, localization algorithm, microphone arrays, microphone location, Microphones, Phase estimation, reverberation, short-time spectral information, Signal processing, source features, source information excitation, speech enhancement, Speech processing, speech production mechanism, speech signal, time-delay, time-delay estimation},
	isbn = {1063-6676},
	doi = {10.1109/TSA.2005.853005},
	author = {Yegnanarayana,B. and Prasanna,S. R.M and Duraiswami, Ramani and Zotkin,Dmitry N}
}
@article {18460,
	title = {Accelerated speech source localization via a hierarchical search of steered response power},
	journal = {IEEE Transactions on Speech and Audio Processing},
	volume = {12},
	year = {2004},
	month = {2004/09//},
	pages = {499 - 508},
	abstract = {Accurate and fast localization of multiple speech sound sources is a problem that is of significant interest in applications such as conferencing systems. Recently, approaches that are based on search for local peaks of the steered response power are becoming popular, despite their known computational expense. Based on the observation that the wavelengths of the sound from a speech source are comparable to the dimensions of the space being searched and that the source is broadband, we have developed an efficient search algorithm. Significant speedups are achieved by using coarse-to-fine strategies in both space and frequency. We present applications of the search algorithm to speed up simple delay-and-sum beamforming and steered response power phase-transform weighted (SRP-PHAT) source localization algorithms. A systematic series of comparisons with previous algorithms are made that show that the technique is much faster, robust, and accurate. The performance of the algorithm can be further improved by using constraints from computer vision.},
	keywords = {accelerated speech source localization, Acceleration, array signal processing, conferencing system, Delay, delay-and-sum beamforming, direction-of-arrival estimation, Frequency, hierarchical search algorithm, Inverse problems, multimedia applications, Multimedia communication, multiple speech sound source, Position measurement, Robustness, search problems, Sensor arrays, Signal processing algorithms, speech, speech enhancement, Speech processing, steered response power, steered response power phase-transform weighted source localization algorithm, transducer arrays, User interfaces},
	isbn = {1063-6676},
	doi = {10.1109/TSA.2004.832990},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani}
}
@article {17469,
	title = {User interface design with speech technologies : A cognitive limitations review},
	journal = {Sprache und Datenverarbeitung},
	volume = {28},
	year = {2004},
	month = {2004///},
	pages = {101 - 109},
	keywords = {Communication homme-machine, computational linguistics, Human-computer communication, interface, Linguistique informatique, Speech processing, Traitement automatique de la parole},
	isbn = {0343-5202},
	url = {http://cat.inist.fr/?aModele=afficheN\&cpsidt=16961708},
	author = {Shneiderman, Ben}
}
@conference {11905,
	title = {Non-parametric expectation maximization: a learning automata approach},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {3},
	year = {2003},
	month = {2003/10//},
	pages = {2996- 3001 vol.3 - 2996- 3001 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The famous expectation maximization technique suffers two major drawbacks. First, the number of components has to be specified apriori. Also, the expectation maximization is sensitive to initialization. In this paper, we present a new stochastic technique for estimating the mixture parameters. Parzen Window is used to estimate a discrete estimate of the PDF of the given data. Stochastic learning automata is then used to select the mixture parameters that minimize the distance between the discrete estimate of the PDF and the estimate of the expectation maximization. The validity of the proposed approach is verified using bivariate simulation data.},
	keywords = {Automatic speech recognition, bivariate simulation data, Computational modeling, Computer vision, Density functional theory, expectation maximization technique, learning automata, mixture parameters estimation, nonparametric expectation maximization, nonparametric statistics, optimisation, parameter estimation, Parzen Window, PDF, probability, probability density function, Speech processing, Speech recognition, stochastic learning automata, Stochastic processes, stochastic technique},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1244347},
	author = {Abd-Almageed, Wael and El-Osery,A. and Smith,C.E.}
}
@conference {18466,
	title = {Active speech source localization by a dual coarse-to-fine search},
	booktitle = {2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP {\textquoteright}01)},
	volume = {5},
	year = {2001},
	month = {2001///},
	pages = {3309-3312 vol.5 - 3309-3312 vol.5},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Accurate and fast localization of multiple speech sound sources is a significant problem in videoconferencing systems. Based on the observation that the wavelengths of the sound from a speech source are comparable to the dimensions of the space being searched, and that the source is broadband, we develop an efficient search strategy that finds the source(s) in a given space. The search is made efficient by using coarse-to-fine strategies in both space and frequency. The algorithm is shown to be robust compared to typical delay-based estimators and fast enough for real-time implementation. Its performance can be further improved by using constraints from computer vision},
	keywords = {active speech source localization, array signal processing, Computer interfaces, Delay effects, delay estimation, delay-based estimators, dual coarse-to-fine search, Frequency, Inverse problems, Laboratories, multiple speech sound sources, Position measurement, real-time implementation, Sensor arrays, Signal processing algorithms, space, speech, Speech processing, Teleconferencing, videoconferencing systems},
	isbn = {0-7803-7041-4},
	doi = {10.1109/ICASSP.2001.940366},
	author = {Duraiswami, Ramani and Zotkin,Dmitry N and Davis, Larry S.}
}
@article {15045,
	title = {VLSI Architectures and Implementation of Predictive Tree- Searched Vector Quantizers for Real-Time Video Compression},
	volume = {ISR-TR-1992-48},
	year = {1992},
	month = {1992///},
	institution = {Institute for Systems Research, University of Maryland, College Park},
	abstract = {We describe a pipelined systolic architecture for implementing predictive Tree-Searched Vector Quantization (PTSVQ) for real- time image and speech coding applications. This architecture uses identical processors for both the encoding and decoding processes. the overall design is regular and the control is simple. Input data is processed at a rate of 1 pixel per clock cycle, which allows real-time processing of images at video rates. We implemented these processors using 1.2um CMOS technology. Spice simulations indicate correct operation at 40 MHz. Prototype version of these chips fabricated using 2um CMOS technology work at 20 MHz.},
	keywords = {data compression, IMAGE PROCESSING, Signal processing, Speech processing, Systems Integration, systolic architecture, Vector quantization, VLSI architectures},
	url = {http://drum.lib.umd.edu/handle/1903/5230},
	author = {Yu,S.-S. and Kolagotla,Ravi K. and JaJa, Joseph F.}
}
