Data pruning for template-based automatic speech recognition

In this paper we describe and analyze a data pruning method in combination with template-based automatic speech recognition. We demonstrate the positive effects of polishing the template database by minimizing the word error rate scores. Data pruning allowed to effectively reduce the database size, and therefore the model size, by an impressive 30%, with consequent benefits on the computation time and memory usage.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Dino Seppi, Dirk Van Compernolle
• Keywords: Data Pruning Method, INTERSPEECH 2010, Signal Processing, Template-based Automatic Speech, Word Error Rate