Isolated word recognition using a two-pass pattern recognition approach

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, improvements in discriminability among similar words can be achieved. 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 a locally weighted distance is used to provide optimal separation among words in the chosen class (or classes) and the recognition decision is made on the basis of these local distance scores. For a highly complex vocabulary (letters of the alphabet, digits, and 3 command words) recognition improvements of from 3 to 7 percent were obtained using the two-pass recognition strategy.