A Subband-Based SVM Front-End for Robust ASR

This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using ensemble methods are addressed. The proposed front-end is compared with state-of-the-art ASR front-ends in terms of robustness to additive noise and linear filtering. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of the proposed subband based SVM front-end: it outperforms the standard cepstral front-end in the presence of noise and linear filtering for signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed front-end with a conventional front-end such as MFCC yields further improvements over the individual front ends across the full range of noise levels

Towards Robust Phoneme Classification with Hybrid Features

Abstract—In this paper, we investigate the robustness of phoneme classification to additive noise with hybrid features using support vector machines (SVMs). In particular, the cepstral features are combined with short term energy features of acoustic waveform segments to form a hybrid representation. The energy features are then taken into account separately in the SVM kernel, and a simple subtraction method allows them to be adapted effectively in noise. This hybrid representation contributes significantly to the robustness of phoneme classification and narrows the performance gap to the ideal baseline of classifiers trained under matched noise conditions. Index Terms—Hybrid features, Phoneme classification, Robustness, Support vector machine

Assessment and Evaluation Framework with Successful Application in ABET Accreditation

The development of reliable and easy-to-deploy assessment plans are a world-wide concern of academic programs. The cultivation of a culture of assessment or engaging in an accreditation effort is dependent on the development of effective assessment frameworks. Examining large variety of sources and using different tools challenge the applicability of assessment plans and can prove to be major hurdles. In this paper, a unified framework is proposed that enables the assessment and evaluation of student outcomes, at the program level, and evaluating student performance as well. The proposed framework identifies a set of courses to be assessed using direct tools. The tools enable measurements of attainment scores at the course learning outcomes, performance indicators, and student outcome levels to create a paradigm for unified assessment. The framework was deployed within a two cycles application and led to a successful accreditation of a computer engineering program by ABET