  With the evolution of neural network based methods, automatic speech
recognition (ASR) field has been advanced to a level where building an
application with speech interface is a reality. In spite of these advances,
building a real-time speech recogniser faces several problems such as low
recognition accuracy, domain constraint, and out-of-vocabulary words. The low
recognition accuracy problem is addressed by improving the acoustic model,
language model, decoder and by rescoring the N-best list at the output of the
decoder. We are considering the N-best list rescoring approach to improve the
recognition accuracy. Most of the methods in the literature use the
grammatical, lexical, syntactic and semantic connection between the words in a
recognised sentence as a feature to rescore. In this paper, we have tried to
see the semantic relatedness between the words in a sentence to rescore the
N-best list. Semantic relatedness is computed using
TransE~\cite{bordes2013translating}, a method for low dimensional embedding of
a triple in a knowledge graph. The novelty of the paper is the application of
semantic web to automatic speech recognition.
