CIEASR:Contextual Image-Enhanced Automatic Speech Recognition for Improved Homophone Discrimination
Year: 2024
Type: conference-paper
Abstract: Automatic Speech Recognition (ASR) models pre-trained on large-scale speech datasets have achieved significant breakthroughs compared with traditional methods. However, mainstream pre-trained ASR models encounter challenges in distinguishing homophones, which have close or identical pronunciations. Previous studies have introduced visual auxiliary cues to address this challenge, yet the sophisticated use of lip movements falls short in correcting homophone errors. On the other hand, the fusion and utilization of scene images remain in an exploratory stage, with performance still inferior to the pre-trained speech model. In this paper, we introduce CIEASR (Contextual Image-Enhanced Automatic Speech Recognition), a novel multimodal speech recognition model that incorporates a new cue fusion method, using scene images as soft prompts to correct homophone errors. To mitigate data scarcity, we refine and expand the VSDial dataset for extensive experiments, illustrating that scene images contribute to the accurate recognition of entity nouns and personal pronouns. Our proposed CIEASR achieves state-of-the-art results on VSDial and Flickr8K, significantly reducing the Character Error Rate (CER) on VSDial from 3.61% to 0.92%.
Authors: Ziyi Wang, Yiming Rong, Deyang Jiang, Haoran Wu, Shiyu Zhou, Bo Xu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403791377