Year: 1999
Type: dissertation
Abstract: The acoustic-phonetic modeling component of most current speech recognition systems calculates a small set of homogeneous frame-based measurements at a single, fixed time-frequency resolution.This thesis presents evidence indicating that recognition performance can be significantly improved through a contrasting approach using more detailed and more diverse acoustic measurements, which we refer to as heterogeneous measurements.This investigation has three principal goals.The first goal is to develop heterogeneous acoustic measurements to increase the amount of acoustic-phonetic information extracted from the speech signal.Diverse measurements are obtained by varying the time-frequency resolution, the spectral representation, the choice of temporal basis vectors, and other aspects of the preprocessing of the speech waveform.The second goal is to develop classifier systems for successfully utilizing high-dimensional heterogeneous acoustic measurement spaces.This is accomplished through hierarchical and committee-based techniques for combining multiple classifiers.The third goal is to increase understanding of the weaknesses of current automatic phonetic classification systems.This is accomplished through perceptual experiments on stop consonants which facilitate comparisons between humans and machines.Systems using heterogeneous measurements and multiple classifiers were evaluated in phonetic classification, phonetic recognition, and word recognition tasks.On the TIMIT core test set, these systems achieved error rates of 18.3% and 24.4% for context-independent phonetic classification and context-dependent phonetic recognition, respectively.These results are the best that we have seen reported on these tasks.Word recognition experiments using the corpus associated with the JUPITER telephone-based weather information system showed 10-16% word error rate reduction, thus demonstrating that these techniques generalize to word recognition in a telephone-bandwidth acoustic environment.
Authors: Andrew K. Halberstadt, James Glass
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W30596880