@conference {
	title = {Robust Recognition of Cellular Telephone Speech by Adaptive Vector Quantization},
	author = {Sonmez, Kemal M., Rajasekaran, Raja., Baras, John S.},
	year = {1996},
	month = {05/1996},
	abstract = {The performance degradation as a result of acoustical environment mismatch remains an important practical problem in speech recognition. The problem carries a greater significance in applications over telecommunication channels, especially with the wider use of personal communications systems such as cellular phones which invariably present challenging acoustical conditions. In this work, we introduce a vector quantization (VQ) based compensation technique which both makes use of a priori information about likely acoustical environments and adapts to the test environment to improve recognition. The technique is progressive and requires neither simultaneously recorded speech from the training and the testing environments nor EM-type batch iterations. Instead of using simultaneously recorded data, the integrity of the updated VQ codebooks with respect to acoustical classes is maintained by endowing the codebooks with a topology of reference environment. We report results on the McCaw Cellular Corpus where the technique decreases the word error for continuous ten digit recognition of cellular hands-free microphone speech with land line trained models from 23.8\% to 13.6\% and the speaker dependent voice calling sentence error from 16.5\% to 10.6\%.},
	address = {Atlanta, Georgia},
	booktitle = {IEEE (ICASSP 96) Conference on Acoustics, Speech, and Signal Processing},
	pages = {pp. 503-506}
}
@conference {
	title = {A Statistical Complexity Framework for Topology Preserving Adaptive Vector Quantization},
	author = {Sonmez, Kemal M., Baras, John S.},
	year = {1996},
	month = {03/1996},
	abstract = {We propose a statistical model complexity framework for topology preserving adaptive vector quantization. In this setting, the adaptation of the neighborhood function during training of the codebooks, which is essential for producing global organization, may be regarded as increasing the statistical model complexity as more data become available. Therefore, the training is equivalent to on the fly optimization of the bias/variance trade-off.},
	address = {Princeton, NJ},
	booktitle = {CISS\&rsquo;96}
}
@conference {
	title = {The Multiple LAG Process and Its Estimation},
	author = {Sonmez, Kemal M., Baras, John S.},
	year = {1995},
	month = {06/1995},
	abstract = {We derive estimators for the multiple lag process, a generalization of the lag process, via spectral representations of stationary processes by complex random spectral measures. We present estimators of transfer functions for the multiple lag model with a given vector of lags and derive a multiple-lag (quadratic) coherence which can be maximized to choose the best vector of lags in the minimum mean squared error sense from a given set of lag vectors. We also demonstrate the estimation scheme by a simulation example and point out possible applications for the multiple-lag model in speech processing.},
	address = {Detroit, MI},
	booktitle = {The International Conference on Acoustics, Speech and Signal Processing},
	pages = {pp. 1697-1700}
}
@conference {
	title = {Time Series Modeling by Perceptrons: A Likelihood Approach},
	author = {Sonmez, Kemal M., Baras, John S.},
	year = {1993},
	month = {07/1993},
	abstract = {We consider neural network learning problems in which the objective is to learn the relationship between the inputs and the probability distribution of a proposition. We regard successive truth values of the proposition as a dependent binary time series whose instantaneous probability of truth is a function of the past behavior of the joint process between the analog inputs and binary output truth values. In this context, we identify the gradient descent learning algorithm using the Kullback-Leibler relative entropy cost function on a perceptron with a Maximum~Partial Likelihood (MPL) estimator of the perceptron model for the probability of a binary event in terms of its covariates. The implications of this result are: (i) The neural network models obtained by relative entropy learning are shown to have the nice large sample (i.e. training set size) characteristics of MPL estimates: consistency and asymptotic normality. (ii) An important and widely used statistical inference technique, logistic regression, can efficiently be implemented on analog perceptrons for time series modeling and prediction.},
	address = {Portland, Oregon},
	booktitle = {The World Congress on Neural Networks},
	pages = {pp. 601-604}
}
@conference {
	title = {Maximum Partial Likelihood Estimation with Perceptrons},
	author = {Sonmez, Kemal M., Baras, John S.},
	year = {1993},
	month = {03/1993},
	abstract = {We show the equivalence of two techniques of time series modeling/prediction; (ii) perceptron learning of probability distribution of the truth value of a proposition from first order stochastic density approximations, (ii) Maximum Partial Likelihood (MPL) estimation of the parameters of a logistic regressive model for binary time series. This result provides large training set characteristics for the approximate Kullback-Leibler relative entropy learning scheme.},
	address = {Baltimore, Maryland},
	booktitle = { The 1993 Conference on Information Sciences and Systems},
	pages = {pp. 812-817},
	organization = {Johns Hopkins University}
}
