Multilingual End-To-End Spoken Language Understanding For Ultra-Low Footprint Applications
Markus Müller, Anastasios Alexandridis, Zach Trozenski, Joel Whiteman, Grant P. Strimel, Nathan Susanj, Athanasios Mouchtaris, Siegfried Kunzmann
Abstract
Tiny Signal-to-Interpretation (TinyS2I) has been recently introduced as an ultra low-footprint end-to-end spoken language understanding (SLU) model. This architecture is capable of running in ultra resource constrained environments like voice assistant devices, while at the same time reducing latency. In this work, we propose an extension to TinyS2I and train a multilingual system supporting several languages. Multilingual TinyS2I models show little to no degradation compared to their monolingual counterparts. Increasing the network size in width and depth improves the classification accuracy for mono- and multilingual setups, with the multilingual one improving beyond the monolingual accuracy. This enables users to interact with the device in the language of their choice and dynamically switch between languages without an explicit language setting or accuracy degradation.
BibTeX
@inproceedings{icassp2023_multilingualendt,
title = {Multilingual End-To-End Spoken Language Understanding For Ultra-Low Footprint Applications},
author = {Markus Müller and Anastasios Alexandridis and Zach Trozenski and Joel Whiteman and Grant P. Strimel and Nathan Susanj and Athanasios Mouchtaris and Siegfried Kunzmann},
booktitle = {ICASSP 2023},
year = {2023}
}