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Jack McLaughlin

Principal Engineer





Research Interests

Machine Learning, Speech Processing, Speech Recognition, Signal Processing


Dr. Jack McLaughlin joined the Applied Physics Laboratory in October of 2001 as a Principal Engineer. He has been involved in a number of programs involving signal classification and feature extraction with applications in areas such as sonar signal analysis, speech classification and cybersecurity. As Principal Investigator, he has led research programs on mine detection and classification (funded by the Office of Naval Research) and programs on speaker identification (funded by Air Force Research Laboratory). Prior to joining APL-UW, Dr. McLaughlin worked at MIT Lincoln Laboratory where he was involved in all aspects of their speaker identification work. This included clustering, channel mismatch studies, classification of short utterances, work with extremely noisy military radio data, and participation in NIST evaluations. Dr. McLaughlin also spent a year working on speech recognition for telephony applications at a startup company before arriving at the Laboratory.


B.S. Physics, Worcester Polytechnic Institute, 1986

M.S. Electrical Engineering, Boston University, 1992

Ph.D. Electrical Engineering, University of Washington, 1997


2000-present and while at APL-UW

A technique for adjusting Gaussian mixture model weights that improves speaker identification performance in the presence of phonemic train/test mismatch

McLaughlin, J., and L. Owsley, "A technique for adjusting Gaussian mixture model weights that improves speaker identification performance in the presence of phonemic train/test mismatch," J. Acoust. Soc. Am., 129, 2423, doi:10.1121/1.3587917, 2011.

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1 Apr 2011

Speaker identification is complicated by cases where training material is phonemically deficient. Misclassifications can result either because subsequent test material from that speaker contains primarily the phonemes missing from the training data or because that test material is phonemically most consistent with another talker's model. This situation can arise in any dialog where, for reasons of brevity and clarity, conventions must be imposed on phraseology. We present here a technique for detecting phonemic deficiencies in a speaker model, and then correcting that model to partially compensate for the biased training data. This technique relies upon a specially constructed universal background model (UBM) from which speaker models are adapted. This UBM is formed by weighting several dozen phoneme GMMs using EM training. As a result, each Gaussian component of the UBM (and of the resulting speaker models) corresponds to a specific phoneme. Analysis of the speaker model weights reveals whether the training data had the typical phonemic variety found in ordinary speech, and if it did not, the weights are adjusted. Using a specially designed corpus created from the TIMIT utterances, we show that this reweighting technique improves performance over non-reweighted models. Results are also given for the Air Traffic Control Corpus.

Application of low-frequency methods for estimating object size

McLaughlin, J., B. Hamschin, and G. Okopal, "Application of low-frequency methods for estimating object size," J. Acoust. Soc. Am., 129, 2663, doi:10.1121/1.3588906, 2011.

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1 Apr 2011

Classification of submerged objects has traditionally been performed using high frequency sonars and imaging techniques. While this permits fine matching of target templates to images acquired in the field, HF methods are necessarily limited in range due to absorption of sound by the water. LF sonars, while offering increased detection range, come with some significant challenges related to the limited bandwidth available. Nonetheless, we show that it is feasible to estimate object size using nonimaging techniques. There are a number of low-frequency phenomena that can be exploited to this end. Among these are edge diffraction in which sharply angled facets of objects ("edges") act like independent, radiating point sources, and helical waves, which can be set up in cylindrical objects. We show that with appropriate postprocessing of these returns, object edges can be localized thus allowing object extent to be assessed. In this paper, we describe our processing system, and then give results when this system is applied to over 40 sequences of returns from a rail system. In each sequence, a single solid, proud cylinder is insonified, and our system reports an estimate of cylinder length and radius. Histograms of these estimates cluster roughly around the true values.

Improving target tracking performance by incorporating classification information

Hanusa, E., W.H. Mortensen, D.W. Krout, and J. McLaughlin,"Improving target tracking performance by incorporating classification information," In Proceedings, MTS/IEEE OCEANS 2010, Seattle, 20-23 September, doi:10.1109/OCEANS.2010.5664500 (MTS/IEEE, 2010).

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20 Sep 2010

This paper presents approaches for incorporating classification information into target tracking algorithms, specifically in a multistatic active sonar context. In addition, this paper describes the framework designed for simulation and classification of return time series from simulated targets and clutter in a realistic underwater environment. The simulated target and clutter returns are integrated into an existing contact-based tracking dataset (TNO Blind dataset) for which time series are unavailable. Simulations compare the integrating classification of contacts at different stages of tracking algorithms. Results show improvements in some tracking metrics with no degradation of the others.

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Modeling acoustic target response by component

Owsley, L., and J. McLaughlin, "Modeling acoustic target response by component," In Proceedings, MTS/IEEE OCEANS 2010, Seattle, 20-23 September, doi:10.1109/OCEANS.2010.5664273 (MTS/IEEE, 2010).

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20 Sep 2010

The scattered acoustic response of underwater objects due to active interrogation has been studied for decades for use in detection and classification applications. As a means of detection, fielded applications date back nearly a hundred years. However, use of responses for robust automated classification has lagged behind, particularly when the internal structure of the objects is of key importance and when the objects may be partially or fully buried. Analytic solutions for simple geometries have provided much understanding of certain physical mechanisms, but transfer to complex structures of practical importance has proven difficult.

In recent decades, finite element (FE) modeling has provided a method of accurate simulation of many structures previously considered intractable. However, simulation of such complex objects produces equally complex returns, with the result that the models are often simply considered as a "black box" where the physical interpretation of the response components is tenuous at best. Thus the state of the art is still short of a method for development of robust classification systems for complex objects based on the physics of the objects of interest and the varied conditions under which they may be found. This paper introduces an effort to use FE techniques to simulate individual components of a return by "turning off" most aspects of the physics and allowing the researcher to isolate one mechanism at a time. The goal is a true physical understanding of the complete response, a physically justifiable feature set for classification, and a much simpler path to environmental robustness.

Using speed technology to enhance isotope ID and classification

Owsley, L.M.D., J.J. McLaughlin, L.G. Cazzanti, and S.R. Salaymeh, "Using speed technology to enhance isotope ID and classification," Conference Record, Nuclear Science Symposium, Orlando, 24 October-1 November, 629-635, doi:10.1109/NSSMIC.2009.5402002 (IEEE, 2009).

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24 Oct 2009

Scientific advances are often made when researchers identify mathematical or physical commonalities between different fields and are able to apply mature techniques or algorithms developed in one field to another field which shares some of the same challenges. The authors of this paper have identified similarities between the unsolved problems faced in gamma-spectroscopy for automated radioisotope identification and the challenges of the much larger body of research in speech processing. In this paper we describe such commonalities and use them as a motivation for a preliminary investigation of the applicability of speech processing methods to gamma-ray spectra. This approach enables the development of proof-of-concept isotope classifiers, whose performance is presented for both simulated and field-collected gamma-ray spectra.

Combined screener/classifier architectures for mine countermeasures

McLaughlin, J., and L. Owsley, "Combined screener/classifier architectures for mine countermeasures," U.S. Navy J. Underwater Acoust., 58, 1247-1260, 2008.

1 Jan 2008

Speaker Detection and Tracking

McLaughlin, J., and L. Owsley, "Speaker Detection and Tracking," APL-UW TR 0602, August 2006.

30 Aug 2006

Classification of impulsive source sonar echoes in the presence of dispersion

Pitton, J., W. Kooiman, J. McLaughlin, and P. Loughlin, "Classification of impulsive source sonar echoes in the presence of dispersion," Proceedings, 33rd Annual Meeting of the Technical Cooperation Program, 18-21 October, Newport, RI (2004).

18 Oct 2004

Non-stationary signal classification using joint frequency analysis

Sukittanon, S., L. Atlas, J. Pitton, and J. McLaughlin, "Non-stationary signal classification using joint frequency analysis," Proceedings, IEEE International Conference in Acoustics, Speech, and Signal Processing, 6-10 April 2003, Hong Kong, ROC, VI - 453-6, vol.6, doi:10.1109/ICASSP.2003.1201716; (IEEE, 2003).

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10 Apr 2003

Time-varying short-term spectral estimates have been successfully applied in many classification tasks. However, they are still insufficient for many non-stationary signals where time-varying information is useful. We propose to improve the deficiencies of current short-term feature analysis by adding information to describe the time-varying behavior of the signals. Our proposed method, which is motivated by the human auditory system, can be applied to several non-stationary signal types. Real world communication signals were used for experimental verification. These experimental results, assessed with a conventional probabilistic classifier, showed significant improvement when the new features were added to short-term spectral estimates.

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center