Abstract:
This paper investigates the use of graph metrics to further enhance the performance of a language model smoothing algorithm. Bin-Based Ontological Smoothing has been succ...Show MoreMetadata
Abstract:
This paper investigates the use of graph metrics to further enhance the performance of a language model smoothing algorithm. Bin-Based Ontological Smoothing has been successfully used to improve language model performance in automatic speech recognition tasks. It uses ontologies to estimate novel utterances for a given language model. Since ontologies can be represented as graphs, we investigate the use of graph metrics as an additional smoothing factor in order to capture additional semantic or relational information found in ontologies. More specifically, we investigate the effect of HITS, PageRank, Modularity, and weighted degree, on performance. The entire power set of bins is evaluated. Our results show that the interpolation of the original bins at distances 1, 3 and 5 resulted in an improvement in WER of 0.71% relative over the interpolation of bins 1 to 5. Furthermore, modularity, PageRank and HITS show promise for further study.
Date of Conference: 29 August 2016 - 02 September 2016
Date Added to IEEE Xplore: 01 December 2016
ISBN Information:
Electronic ISSN: 2076-1465