Multilingual recognition of non-native speech using acoustic model transformation and pronunciation modeling |
| |
Authors: | G Bouselmi D Fohr I Illina |
| |
Affiliation: | (1) Speech Group, LORIA-CNRS & INRIA, BP 239, 54600 Vandoeuvre-les-Nancy, France;; |
| |
Abstract: | This article presents an approach for the automatic recognition of non-native speech. Some non-native speakers tend to pronounce
phonemes as they would in their native language. Model adaptation can improve the recognition rate for non-native speakers,
but has difficulties dealing with pronunciation errors like phoneme insertions or substitutions. For these pronunciation mismatches,
pronunciation modeling can make the recognition system more robust. Our approach is based on acoustic model transformation
and pronunciation modeling for multiple non-native accents. For acoustic model transformation, two approaches are evaluated:
MAP and model re-estimation. For pronunciation modeling, confusion rules (alternate pronunciations) are automatically extracted
from a small non-native speech corpus. This paper presents a novel approach to introduce confusion rules in the recognition
system which are automatically learned through pronunciation modelling. The modified HMM of a foreign spoken language phoneme
includes its canonical pronunciation along with all the alternate non-native pronunciations, so that spoken language phonemes
pronounced correctly by a non-native speaker could be recognized. We evaluate our approaches on the European project HIWIRE non-native corpus which contains English sentences pronounced by French, Italian, Greek and Spanish speakers. Two cases are
studied: the native language of the test speaker is either known or unknown. Our approach gives better recognition results
than the classical acoustic adaptation of HMM when the foreign origin of the speaker is known. We obtain 22% WER reduction
compared to the reference system. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|