Speaker-Independent Recognition of Isolated Words Using Clustering Techniques

Lawrence R. Rabiner, Stephen E. Levinson, Aaron E. Rosenberg, Jay G. Wilpon

Research output: Contribution to journalArticlepeer-review


A speaker-independent isolated word recognition system is described which is based on the use of multiple templates for each word in the vocabulary. The word templates are obtained from a statistical clustering analysis of a large database consisting of 100 replications of each word (i.e., once by each of 100 talkers). The recognition system, which accepts telephone quality speech input, is based on an LPC analysis of the unknown word, dynamic time warping of each reference template to the unknown word (using the Itakura LPC distance measure), and the application of a K-nearest neighbor (KNN) decision rule. Results for several test sets of data are presented. They show error rates that are comparable to, or better than, those obtained with speaker-trained isolated word recognition systems.

Original languageEnglish (US)
Pages (from-to)336-349
Number of pages14
JournalIEEE Transactions on Acoustics, Speech, and Signal Processing
Issue number4
StatePublished - Aug 1979
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing


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