This paper is concerced with the use of error back-propagation in phonetic classification. Our objective is to investigate the ba(cid:173) sic characteristics of back-propagation, and study how the frame(cid:173) work of multi-layer perceptrons can be exploited in phonetic recog(cid:173) nition. We explore issues such as integration of heterogeneous sources of information, conditioll that can affect performance of phonetic classification, internal representations, comparisons with traditional pattern classification techniques, comparisons of differ(cid:173) ent error metrics, and initialization of the network. Our investiga(cid:173) tion is performed within a set of experiments that attempts to rec(cid:173) ognize the 16 vowels in American English independent of speaker. Our results are comparable to human performance.

This paper is concerced with the use of error back-propagation in phonetic classification. Our objective is to investigate the basic characteristicsof back-propagation, and study how the framework ofmulti-layer perceptrons can be exploited in phonetic recognition.

This paper is concerced with the use of error back-propagation in phonetic classification. Our objective is to investigate the basic characteristics of back-propagation, and study how the framework of multi-layer perceptrons can be exploited in phonetic recognition.

This paper is concerced with the use of error back-propagation in phonetic classification. Our objective is to investigate the basic characteristics of back-propagation, and study how the framework of multi-layer perceptrons can be exploited in phonetic recognition.