| Presentation # | 6 |
| Session: | ASR I |
| Location: | Kallirhoe Hall |
| Session Time: | Wednesday, December 19, 10:00 - 12:00 |
| Presentation Time: | Wednesday, December 19, 10:00 - 12:00 |
| Presentation: |
Poster
|
| Topic: |
Speech recognition and synthesis: |
| Paper Title: |
DYNAMIC EXTENSION OF ASR LEXICON USING WIKIPEDIA DATA |
| Authors: |
Badr Abdullah, Irina Illina, Dominique Fohr, LORIA/INRIA, France |
| Abstract: |
Despite recent progress in developing Large Vocabulary Continuous Speech Recognition Systems (LVCSR), these systems suffer from Out-Of-Vocabulary words (OOV). In many cases, the OOV words are Proper Nouns (PNs). The correct recognition of PNs is essential for broadcast news, audio indexing, etc. In this article, we address the problem of OOV PN retrieval in the framework of broadcast news LVCSR. We focused on dynamic (document dependent) extension of LVCSR lexicon. To retrieve relevant OOV PNs, we propose to use a very large multi-topic text corpus: Wikipedia. This corpus contains a huge number of PNs. These PNs are grouped in semantically similar classes using word embedding. We use a two-step approach: first, we select OOV pertinent classes with a multi-class Deep Neural Network (DNN). Secondly, we rank the OOVs of the selected classes. The experiments on French broadcast news show that the Bi-GRU model outperforms other studied models. Speech recognition experiments demonstrate the effectiveness of the proposed methodology. |