Multilingual Speech Command Recognition with Language Identification

Artur Muratov, Askat Kuzdeuov, Hüseyin Atakan Varol

2025

Data confidence 70 medium

How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Multilingual Speech Command Recognition (SCR) facilitates voice interaction in environments where multiple languages are used interchangeably, a common characteristic of multilingual regions. In such settings, SCR and language identification (LID) are handled by separate models. This separation increases inference time, energy consumption, and memory usage. To address this gap, we propose a unified multitask model that performs SCR and LID simultaneously using a shared encoder and two task-specific output heads. We tested our approach using 15 languages: English, Kazakh, Tatar, Russian, Arabic, Turkish, French, German, Catalan, Spanish, Polish, Dutch, Persian, Kinyarwanda, and Italian. We trained and compared a monolingual SCR model for each language, a multilingual SCR model without LID, a multitask multilingual SCR model with LID, and an LID-only model. The multitask model achieves an average accuracy of 90.73% for SCR and 90.99% for LID, outperforming both the multilingual SCR model without LID and the LID-only model. We have made the source code and pretrained models available at https://github.com/IS2AI/Keyword-MLP-LangID to promote research in this area.