Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition
This addresses accuracy issues in ASR for real-time applications, but it is incremental as it builds on existing rescoring methods.
The paper tackled the problem of low recognition accuracy in automatic speech recognition by using a knowledge graph to compute semantic relatedness between words for rescoring the N-best list, resulting in improved accuracy, though no specific numbers are provided.
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.