J. R. Deller

Least-square identification with error bounds for real-time signal processing and control

Set-membership (SM) identification, which refers to a class of algorithms using certain a priori knowledge about a parametric model to constrain the solutions to certain sets, is considered. The focus is on a class of SM-based techniques that are of particular interest in applications requiring real-time processing. The optimal bounding ellipsoid (OBE) algorithms are interpreted as a blending of the classical least-square error minimization approach with knowledge of bounds on model errors arising from SM considerations. Using this interpretation, a general framework embracing all currently used OBE algorithms is developed, and strategies for adaptation and for implementation on parallel machines are discussed. Computational complexity benefits are considered for the various algorithms. The treatment is tutorial, leaving many of the formal details to an appendix that presents an archival theoretical treatment of the key results. A second appendix gives an overview of current research in the general SM identification field. >

Linear prediction analysis of speech based on set-membership theory

Abstract When computing linear prediction (LP) parameters of speech, large numbers of data are uninformative in a certain set-theoretic sense, and the expense of updating the estimates at these times can be avoided. “Set-membership” (SM) identification is formulated as a weighted recursive covariance LP problem with a special criterion for dynamic weight determination. An algorithm is developed which can be implemented on a systolic processor if desired, but which retains a simple interpretation as a specially weighted convariance LP method. The algorithm is applied to identification of the LP parameters of real speech data, and a number of practical issues are discussed. The potential for an adaptive strategy and other open research questions generated by the experimental work are discussed in the conclusions.

On the use of hidden Markov modelling for recognition of Dysarthric speech

Recognition of the speech of severely dysarthric individuals requires a technique which is robust to extraordinary conditions of high variability and very little training data. A hidden Markov model approach to isolated word recognition is used in an attempt to automatically model the enormous variability of the speech, while signal preprocessing measures and model modifications are employed to make better use of the existing data. Two findings are contrary to general experience with normal speech recognition. The first is that an ergodic model is found to outperform a standard left-to-right (Bakis) model structure. The second is that automated clipping of transitional acoustics in the speech is found to significantly enhance recognition. Experimental results using utterances of cerebral palsied persons with an array of articulatory abilities are presented.

An alternative adaptive sequential regression algorithm and its application to the recognition of cerebral palsy speech

An algorithm is presented which can be used to compute a temporally adaptive, recursive solution to the problem of estimating the linear prediction parameters of speech. The method is theoretically equivalent to conventional adaptive sequential regression, but is slightly more efficient and computationally much more straightforward. Application to the problem of recognition of speech of the profoundly disabled is discussed.

Implementing the optimal bounding ellipsoid algorithm on a fast processor

It is shown that the optimal bounding ellipsoid (OBE) algorithm for identifying an ARMAX system can be formulated as a conventional weighted recursive least squares estimator with special weights. In this framework the OBE can be implemented using contemporary algorithms developed for least squares solutions on systolic machines. An example of a systolic processor for OBE is given, and computational complexity issues are considered.<<ETX>>

Automatic Classification of Laryngeal Dysfunction Using the Roots of the Digital Inverse Filter

An automated technique is presented which employs the systems identification properties of the digital inverse filter (IF) [8] for the classification and assessment of laryngeal dysfunction. The information is contained in the positions of the IF polynomial zeros in the complex plane as the IF is computed repeatedly over small analysis segments of a speech sample. A graphic display of the z-plane roots and a vector of pattern features of that display result for each case. The vectors are then processed by an automated clustering procedure to classify the cases in the feature space. The results of the analysis of a large test battery of acoustically degraded synthetic vowel sounds using the IF method are presented.

An AI-based communication system for motor and speech disabled persons: design methodology and prototype testing

An intelligent communication device is developed to assist nonverbal, motor-disabled persons in the generation of written and spoken messages. The device is centered on a knowledge base of the grammatical rules and message elements. A belief reasoning scheme based on both the information from external sources and the embedded knowledge is used to optimize the process of message search. The search for the message elements is conceptualized as a path search in the language graph, and a special frame architecture is used to construct and to partition the graph. Bayesian belief reasoning from the Dempster-Shafer theory of evidence is augmented to cope with time-varying evidence. An information fusion strategy is also introduced to integrate various forms of external information. Experimental testing of the prototype system is discussed.<<ETX>>

Set-membership theory applied to linear prediction analysis of speech

The theory of set membership (SM) identification is formulated, and applied to linear prediction (LP) analysis of speech. The LP parameters of a simulated vowel are identified as an illustration. The SM strategy results in a significant computational savings due to rejection of data which are informationless in the SM sense.

The Whitaker database of dysarthric (cerebral palsy) speech

The Whitaker database is a collection of 19 275 isolated‐word utterances spoken by six persons whose speech spans a broad spectrum of dysarthria due to cerebral palsy. The database additionally contains utterances by a normal speaker which can be used for reference. The vocabulary is divided into two sets—one of 46 words (the ‘‘TI‐46’’ vocabulary consisting of the alphabet, digits, and 10 control words), the other of of 35 words (the ‘‘Grandfather’’ set consisting of phonetically diverse words). The database is available through the electronic mail network for use in studies of recognition, perception, articulation, and other aspects of speech disorders.

Tom, Dick, and Mary discover the DFT

Discrete Fourier techniques are increasingly being taught as material detached from fundamental continuous-time Fourier analysis. The student is left with an unclear understanding, if any, of the very significant relationships between magnitude and phase spectra generated digitally, and the continuous-time signal which is being analyzed. The present article tells the story of three undergraduate students who discover the DFT, armed only with a knowledge of analog methods.<<ETX>>