Abstract
The goal of automatically understanding speech usually has been approached by first simplifying the task—by studying the speech of one talker, by restricting vocabulary, by considering only high-quality signals, by analyzing one language, etc. In addition, much experimental work has been characterized by an emphasis on either the speech-analysis or the pattern-recognition/statistical aspects of the problem. A new procedure is described for automatically determining the language of a connected-speech sample. The parameters extracted from the acoustic signal are a small number of gross linguistic categories that seem to be robust and reasonably independent of talker and channel, yet carry enough information to separate languages. Successful preliminary feasibility studies using manually transcribed parameters, as well as the performance of the procedures currently in use for automatic implementation of the scheme, are described. The effects of inaccuracies in labeling on the identification of a language are considered. Implications of these studies for the implementation of schemes for word identification and speech recognition are discussed.
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