On The Robustness of Phonetic Information In Short-Time Speech Spectra
Résumé
Speech recognition techniques which take fixed-time slices as input to a matcher face the task of mapping from arbitrary pieces of the physical signal to abstract linguistic units. This paper examines the reliability with which individual vector-quantized LPC spectra can be mapped to various sets of acoustic-phonetic classes. The database for the experiments consisted of approximately 130,000 spectra from a pre-labeled corpus of 616 5-digit strings, and classification was performed on the basis of a maximum likehood decision rule. Classification accuracy, when the same database was used for training and testing, ranged from 94.0% for a simple voiced-voiceless distinction to 42 .7% for a set of 45 acoustic-phonetic classes used in earlier connected digit recognition experiments
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