Work

A Comparative Analysis of Bilingual and Trilingual Wav2Vec Models for Automatic Speech Recognition in Multilingual Oral History Archives

Year: 2024

Type: conference-paper

Abstract: In this paper, we are comparing monolingual Wav2Vec 2.0 models with various multilingual models to see whether we could improve speech recognition performance on a unique oral history archive containing a lot of mixed-language sentences. Our main goal is to push forward research on this unique dataset, which is an extremely valuable part of our cultural heritage. Our results suggest that monolingual speech recognition models are, in most cases, superior to multilingual models, even when processing the oral history archive full of mixed-language sentences from non-native speakers. We also performed the same experiments on the public CommonVoice dataset to verify our results. We are contributing to the research community by releasing our pre-trained models to the public.

Source: Interspeech (USB)

DOI: 10.21437/interspeech.2024-472

Authors: Jan Lehečka, Josef V. Psutka, Lubos Smidl, Pavel Ircing, Josef Psutka

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Ministerstvo Vnitra České Republiky

Award: Robust processing of recordings for operations and security

OpenAlex ID: W4402112440

FWCI: 1.903

Cites:

Cited by: 6

Related to: 10