Classification and Vowel Recognition

The ability to recognize and categorize things is fundamental to human cognition; a large part of our ability to understand and deal with the world around us is a result of our ability to classify things. This task, which is generally known as classification, is important enough that we often want to design systems that are capable of recognition and categorization. We want vending machines to be able to recognize the bills inserted into the bill changer. We want internet search engines to classify web pages based on their relevance to our query. We want computers that can recognize and classify speech properly so that we can interact with them naturally. We want medical systems that can classify unusual regions of an xray as cancerous or benign. We want high speed digital communication modems that can determine the sequence of, say, 64-ary signals that were transmitted. There is a vast array of applications for classification. In this lab, we consider a popular application of classification: speech recognition. In particular, we will focus on a simplified version of speech recognition, namely, vowel classification. That is, we will experiment with systems that classify a short signal segment, corresponding to a spoken vowel, as either an “ah”, or an “ee”, or an “oh”, etc.. (We won’t deal with how one determines that a given segment corresponds to a vowel.) In the process, we will develop some of the basic ideas behind automatic classification. One of these basic ideas is that an item to be classified is called an instance. For example, if each of 50 short segments of speech must be individually classified, then each segment is considered to be one instance. A second basic idea is that there is a finite set of prespecified classes to which instances may belong. The goal of a classifier system (or simply a classifier) is to determine the class to which a presented instance belongs. A third basic idea is that to simplify the process, the classification of a given instance is based on a set of feature values. This set is a relatively small list of numbers that, to an appropriate degree, describe the given instance. For example, the short segment of speech might contain 1000 samples, but we will see that vowel classification can be based on feature set with as few as two components. A fourth basic idea is that classification is often performed by comparing the feature values for an instance to be classified with sets of feature values that are representative of each class.