Word/sub-word lattices decomposition and combination for speech recognition
Résumé
This paper presents the benefit of using multiple lexical units in the post-processing stage of an ASR system. Since the use of sub-word units can reduce the high out-of-vocabulary rate and improve the lack of text resources in statistical language modeling, we propose several methods to decompose, normalize and combine word and sub-word lattices generated from different ASR systems. By using a sub-word information table, every word in a lattice can be decomposed into sub-word units. These decomposed lattices can be combined into a common lattice in order to generate a confusion network. This lattices combination scheme results in an absolute syllable error rate reduction of about 1.4% over the sentence MAP baseline method for a Vietnamese ASR task. By comparing with the N-best lists combination and voting method, the proposed method works better.
Origine | Fichiers produits par l'(les) auteur(s) |
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