Multi-Level Improvement for a Transcription
Generated by Automatic Speech Recognition
System for Arabic
Heithem Amich, Mohamed Ben
Mohamed, and Mounir Zrigui
LaTICE Laboratory, Monastir Faculty of Sciences, Tunisia
Abstract: In this paper we will propose a novel approach to
improving an automatic speech recognition system. The proposed method
constructs a search space based on the relations of semantic dependence of the
output of a recognition system. Then, it applies syntactic and phonetic filters
so as to choose the most probable hypotheses. To achieve this objective,
different techniques are deployed, such as the word2vec or the language model Recurrent
Neural Networks Language Models (RNNLM) or ever the language model tagged in
addition to a phonetic pruning system. The obtained results showed that the
proposed approach allowed to improve the accuracy of the system especially for
the recognition of mispronounced words and irrelevant words.
Keywords: Automatic speech recognition, multi-level
improvement, language model, semantic similarity, phonetic pruning.