In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
146
Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
146Paperback(2002)
Product Details
| ISBN-13: | 9783540003250 |
|---|---|
| Publisher: | Springer Berlin Heidelberg |
| Publication date: | 02/12/2003 |
| Series: | Lecture Notes in Computer Science , #2560 |
| Edition description: | 2002 |
| Pages: | 146 |
| Product dimensions: | 6.10(w) x 9.25(h) x 0.01(d) |