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Voice, as input, has progressively become popular on mobiles and seems totranscend 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 becatastrophic to the VS system. Building on the recent advanced lightweightautoregressive retrieval model, which has the potential to be deployed onmobiles, leading to a more secure and personal VS assistant. This paperpresents a novel study of VS leveraging autoregressive retrieval and tacklesthe crucial problems facing VS, viz. the performance drop caused by ASR noise,via data augmentations and contrastive learning, showing how explicit andimplicit modeling the noise patterns can alleviate the problems. A series ofexperiments conducted on the Open-Domain Question Answering (ODSQA) confirm ourapproach's effectiveness and robustness in relation to some strong baselinesystems.

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