# Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition

arXiv: https://arxiv.org/abs/1705.08018 · HTML: https://mlsanity.com/paper/1705.08018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 1 of 1)

- [Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition](https://mlsanity.com/paper/2301.02736/) (2023)

## Abstract (arXiv)

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~citebordes2013translating, 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.