Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

Tactual Display of Consonant Voicing to Supplement Lipreading

This research is concerned with the development of tactual displays to supplement the information available through lipreading. Because voicing carries a high informational load in speech and is not well transmitted through lipreading, the efforts are focused on providing tactual displays of voicing to supplement the information available on the lips of the talker. This research includes exploration of 1) signal-processing schemes to extract information about voicing from the acoustic speech signal, 2) methods of displaying this information through a multi-finger tactual display, and 3) perceptual evaluations of voicing reception through the tactual display alone (T), lipreading alone (L), and the combined condition (L+T). Signal processing for the extraction of voicing information used amplitude-envelope signals derived from filtered bands of speech (i.e., envelopes derived from a lowpass-filtered band at 350 Hz and from a highpass-filtered band at 3000 Hz). Acoustic measurements made on the envelope signals of a set of 16 initial consonants represented through multiple tokens of C1VC2 syllables indicate that the onset-timing difference between the low- and high-frequency envelopes (EOA: envelope-onset asynchrony) provides a reliable and robust cue for distinguishing voiced from voiceless consonants. This acoustic cue was presented through a two-finger tactual display such that the envelope of the high-frequency band was used to modulate a 250-Hz carrier signal delivered to the index finger (250-I) and the envelope of the low-frequency band was used to modulate a 50-Hz carrier delivered to the thumb (50T). The temporal-onset order threshold for these two signals, measured with roving signal amplitude and duration, averaged 34 msec, sufficiently small for use of the EOA cue. Perceptual evaluations of the tactual display of EOA with speech signal indicated: 1) that the cue was highly effective for discrimination of pairs of voicing contrasts; 2) that the identification of 16 consonants was improved by roughly 15 percentage points with the addition of the tactual cue over L alone; and 3) that no improvements in L+T over L were observed for reception of words in sentences, indicating the need for further training on this task