Confusion2Vec 2.0: Enriching ambiguous spoken language representations with subwords.

ABSTRACT: Word vector representations enable machines to encode human language for spoken language understanding and processing. Confusion2vec, motivated from human speech production and perception, is a word vector representation which encodes ambiguities present in human spoken language in addition to semantics and syntactic information. Confusion2vec provides a robust spoken language representation by considering inherent human language ambiguities. In this paper, we propose a novel word vector space estimation by unsupervised learning on lattices output by an automatic speech recognition (ASR) system. We encode each word in Confusion2vec vector space by its constituent subword character n-grams. We show that the subword encoding helps better represent the acoustic perceptual ambiguities in human

SUBMITTER: Gurunath Shivakumar P 

PROVIDER: S-EPMC8896703 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature