Calibration of machine scores for pronunciation grading (1998)
| Venue: | Proc. Int'l Conf. on Spoken Language Processing |
| Citations: | 4 - 2 self |
BibTeX
@INPROCEEDINGS{Franco98calibrationof,
author = {Horacio Franco and Leonardo Neumeyer},
title = {Calibration of machine scores for pronunciation grading},
booktitle = {Proc. Int'l Conf. on Spoken Language Processing},
year = {1998}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Our proposed paradigm for automatic assessment of pronunciation quality uses hidden Markov models (HMMs) to generate phonetic segmentations of the student’s speech. From these segmentations, we use the HMMs to obtain spectral match and duration scores. In this work we focus on the problem of calibrating different machine scores to obtain an accurate prediction of the grades that a human expert would assign to the pronunciation. We discuss the application of different approaches based on minimum mean square error (MMSE) estimation and Bayesian classification. We investigate the characteristics of the different mappings as well as the effects of the prior distribution of grades in the calibration database. We finally suggest a simple method to extrapolate mappings from one language to another. 1.
Citations
| 233 | Neural Network Classifiers Estimate Bayesian a posteriori Probabilities - Richard, Lippmann - 1991 |
| 36 | Automatic Text-Independent Pronunciation Scoring of Foreign Language Student Speech - Neumeyer, Franco, et al. - 1996 |
| 34 | GENONES: Optimizing the Degree of Mixture Tying in a Large Vocabulary - Digalakis, Murveit - 1994 |
| 34 | Automatic pronunciation scoring for language instruction - Franco, Neumeyer, et al. - 1997 |
| 21 | Combination of machine scores for automatic grading of pronunciation quality - Franco, Neumeyer, et al. - 2000 |
| 3 | Modeling Intra-Word Pauses - Franco, Neumeyer, et al. - 1998 |
| 1 | Classification and Regression Trees,” The Waxworks & Brooks/Cole Statistics/Probability Series - Brahmin, Friedman, et al. - 1984 |
