The LIMSI Broadcast News transcription system
Speech Communication - Special issue on automatic transcription of broadcast news data
Pattern Recognition in Speech and Language Processing
Pattern Recognition in Speech and Language Processing
Unsupervised learning of the morphology of a natural language
Computational Linguistics
Training neural network language models on very large corpora
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Continuous space language models
Computer Speech and Language
Recent innovations in speech-to-text transcription at SRI-ICSI-UW
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
The Arabic language presents a number of challenges for speech recognition, arising in part from the significant differences in the spoken and written forms, in particular the conventional form of texts being non-vowelized. Being a highly inflected language, the Arabic language has a very large lexical variety and typically with several possible (generally semantically linked) vowelizations for each written form. This article summarizes research carried out over the last few years on speech-to-text transcription of broadcast data in Arabic. The initial research was oriented toward processing of broadcast news data in Modern Standard Arabic, and has since been extended to address a larger variety of broadcast data, which as a consequence results in the need to also be able to handle dialectal speech. While standard techniques in speech recognition have been shown to apply well to the Arabic language, taking into account language specificities help to significantly improve system performance.