Name
Title | ||
|---|---|---|
Automatic pronunciation scoring of words and sentences independent from the non-native's first language |
Abstract | ||
|---|---|---|
This paper describes an approach for automatic scoring of pronunciation quality for non-native speech. It is applicable regardless of the foreign language student's mother tongue. Sentences and words are considered as scoring units. Additionally, mispronunciation and phoneme confusion statistics for the target language phoneme set are derived from human annotations and word level scoring results using a Markov chain model of mispronunciation detection. The proposed methods can be employed for building a part of the scoring module of a system for computer assisted pronunciation training (CAPT). Methods from pattern and speech recognition are applied to develop appropriate feature sets for sentence and word level scoring. Besides features well-known from and approved in previous research, e.g. phoneme accuracy, posterior score, duration score and recognition accuracy, new features such as high-level phoneme confidence measures are identified. The proposed method is evaluated with native English speech, non-native English speech from German, French, Japanese, Indonesian and Chinese adults and non-native speech from German school children. The speech data are annotated with tags for mispronounced words and sentence level ratings by native English teachers. Experimental results show, that the reliability of automatic sentence level scoring by the system is almost as high as the average human evaluator. Furthermore, a good performance for detecting mispronounced words is achieved. In a validation experiment, it could also be verified, that the system gives the highest pronunciation quality scores to 90% of native speakers' utterances. Automatic error diagnosis based on a automatically derived phoneme mispronunciation statistic showed reasonable results for five non-native speaker groups. The statistics can be exploited in order to provide the non-native feedback on mispronounced phonemes. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.csl.2008.03.001 | Computer Speech & Language |
Keywords | Field | DocType |
native english speech,phoneme mispronunciation statistic,mispronunciation detection,scoring module,sentence scoring,non-native speech,word scoring,pronunciation assessment,speech recognition,automatic pronunciation,mispronounced word,automatic scoring,scoring unit,speech data,non-native english speech,foreign language,markov chain model,native speaker | Pronunciation,Statistic,Computer science,Computational linguistics,Markov chain,Speech recognition,Artificial intelligence,Natural language processing,Sentence,First language,Foreign language,German | Journal |
Volume | Issue | ISSN |
23 | 1 | Computer Speech & Language |
Citations | PageRank | References |
21 | 1.39 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Cincarek | 1 | 42 | 5.16 |
| Rainer Gruhn | 2 | 45 | 6.86 |
| Christian Hacker | 3 | 235 | 22.51 |
| Elmar Nöth | 4 | 959 | 158.94 |
| Satoshi Nakamura | 5 | 1099 | 194.59 |