Speech & Signal Processing

EE 658 Speech Signal Processing Pole-zero plot of a reversed decaying exponential

Speech systems are becoming more and more commonplace in today's computer systems. Examples are speech recognition systems and Text-to-Speech synthesis systems. This course will introduce the fundamentals of the underlying speech signal processing that enables such systems. Topics include speech production and perception by humans, frequency transforms, filters, linear predictive features, pitch estimation, speech coding, speech enhancement, and prosodic speech modification.

CS/EE 552/652 Automatic Speech Recognition

This course will provide theoretical foundations and practical experience in speech recognition by machine. Specific topics include feature extraction, dynamic time warping, vector quantization and Gaussian mixture models, hidden Markov models, Viterbi search, training with expectation maximization (EM), decision-tree based clustering, language models, finite-state formulation, and speaker adaptation.

Prerequisites: CS/EE 555/655 Analyzing Sequences, EE 506/606 Speech Signal Processing

 

EE 530/630 Speech Synthesis

This course will introduce students to the problem of synthesizing speech from text input. Speech synthesis is a challenging area that draws on expertise from a diverse set of scientific fields, including signal processing, linguistics, psychology, statistics, and artificial intelligence. Fundamental advances in each of these areas will be needed to achieve truly human-like synthesis quality and advances in other realms of speech technology (like speech recognition, speech coding, speech enhancement). In this course, we will consider current approaches to sub-problems such as text analysis, pronunciation, linguistic analysis of prosody, and generation of the speech waveform. Lectures, demonstrations, and readings of relevant literature in the area will be supplemented by student lab exercises using hands-on tools.

 

EE 584/684 Introduction to Image Processing

This course covers basic image processing principles and techniques with a brief introduction to machine vision. Specific topics include image transform methods, image filtering, image enhancement, image restoration, segmentation, image representation and feature extraction, image recognition and classification, and image compression. Application of these techniques is illustrated in numerous examples. Pre-requisite: probability and statistics or equivalent, calculus, linear algebra and proficiency in at least one high-level programming language.