AMPS: ASR with Multimodal Paraphrase Supervision
Abhishek Gupta, Amruta Parulekar, Sameep Chattopadhyay, Preethi Jyothi
Abstract
Spontaneous or conversational multilingual speech presents many challenges for state-of-the-art automatic speech recognition (ASR) systems. In this work, we present a new technique AMPS, that augments a multilingual multimodal ASR system with paraphrase-based supervision for improved conversational ASR in multiple languages, including Hindi, Marathi, Malayalam, Kannada, and Nyanja. We use paraphrases of the reference transcriptions as additional supervision while training the multimodal ASR model and selectively invoke this paraphrase objective for utterances with poor ASR performance. Using AMPS with a state-of-the-art multimodal model SeamlessM4T, we obtain significant relative reductions in word error rates (WERs) of up to 5%. We present detailed analyses of our system using both objective and human evaluation metrics.
BibTeX
@inproceedings{gupta-etal-2025-amps,
title = "{AMPS}: {ASR} with Multimodal Paraphrase Supervision",
author = "Gupta, Abhishek and
Parulekar, Amruta and
Chattopadhyay, Sameep and
Jyothi, Preethi",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-short.35/",
pages = "404--413",
ISBN = "979-8-89176-190-2"
}