Name
Abstract | ||
|---|---|---|
In this article we present a novel method for automatic pronunciation error detection of children's speech. A phone graph is generated from the audio segment and augmented if necessary with alignments of phonetic transcriptions of the word to score. This graph is used for extracting phone-level features using conventional HMM/GMM acoustic scores and Support Vector Machine (SVM) classifiers acting as probabilistic estimators. Finally an SVM is used to combine the phone-level features extracted for each word to produce a word-based pronunciation score.Experimental results show that the proposed method and features can be effectively used for pronunciation scoring. In particular the detection of mispronunciations is increased more than 22% with respect to the baseline. |
Year | Venue | Keywords |
|---|---|---|
2008 | INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5 | pronunciation scoring, children's speech, phone graphs, Support Vector Machines, posterior probabilities |
Field | DocType | Citations |
Pronunciation,Computer science,Error detection and correction,Speech recognition | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Bolaños | 1 | 20 | 4.16 |
| Wayne Ward | 2 | 1000 | 93.39 |
| Barbara Wise | 3 | 0 | 0.34 |
| Sarel van Vuuren | 4 | 202 | 31.94 |