A Two-Pass Pattern-Recognition Approach to Isolated Word Recognition

Abstract

One of the major drawbacks of the standard pattern-recognition approach to isolated word recognition is that poor performance is generally achieved for word vocabularies with acoustically similar words. This poor performance is related to the pattern similarity (distance) algorithms that are generally used in which a global distance between the test pattern and each reference pattern is computed. Since acoustically similar words are, by definition, globally similar, it is difficult to reliably discriminate such words, and a high error rate is obtained. By modifying the pattern-similarity algorithm so that the recognition decision is made in two passes, we can achieve improvements in discriminability among similar words. In particular, on the first pass the recognizer provides a set of global distance scores which are used to decide a class (or a set of possible classes) in which the spoken word is estimated to belong. On the second pass we use a locally weighted distance to provide optimal separation among words in the chosen class (or classes), and make the recognition decision on the basis of these local distance scores. For a highly complex vocabulary (letters of the alphabet, digits, and three command words), we obtain recognition improvements of from 3 to 7 percent using the two-pass recognition strategy.