AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning

Yi Cheng Wang, Tzu Ting Yang, Hsin Wei Wang, Bi Cheng Yan, Berlin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input. Through voice, the voice search (VS) system can provide a more natural way to meet user's information needs. However, errors from the automatic speech recognition (ASR) system can be catastrophic to the VS system. Building on the recent advanced lightweight autoregressive retrieval model, which has the potential to be deployed on mobiles, leading to a more secure and personal VS assistant. This paper presents a novel study of VS leveraging autoregressive retrieval and tackles the crucial problems facing VS, viz. the performance drop caused by ASR noise, via data augmentations and contrastive learning, showing how explicit and implicit modeling the noise patterns can alleviate the problems. A series of experiments conducted on the Open-Domain Question Answering (ODSQA) confirm our approach's effectiveness and robustness in relation to some strong baseline systems.

Original languageEnglish
Title of host publication2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2331-2335
Number of pages5
ISBN (Electronic)9798350300673
DOIs
Publication statusPublished - 2023
Event2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan
Duration: 2023 Oct 312023 Nov 3

Publication series

Name2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Country/TerritoryTaiwan
CityTaipei
Period2023/10/312023/11/03

Keywords

  • Autoregressive information retrieval
  • Contrastive learning
  • Information retrieval
  • Voice search

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Artificial Intelligence
  • Computer Science Applications

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