31 research outputs found
Automatic Compound Word Reconstruction for Speech Recognitionof Compounding Languages
Proceedings of the 16th Nordic Conference
of Computational Linguistics NODALIDA-2007.
Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit.
University of Tartu, Tartu, 2007.
ISBN 978-9985-4-0513-0 (online)
ISBN 978-9985-4-0514-7 (CD-ROM)
pp. 5-12
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge
This paper describes Tallinn University of Technology (TalTech) systems
developed for the ASRU MADASR 2023 Challenge. The challenge focuses on
automatic speech recognition of dialect-rich Indian languages with limited
training audio and text data. TalTech participated in two tracks of the
challenge: Track 1 that allowed using only the provided training data and Track
3 which allowed using additional audio data. In both tracks, we relied on
wav2vec2.0 models. Our methodology diverges from the traditional procedure of
finetuning pretrained wav2vec2.0 models in two key points: firstly, through the
implementation of the aligned data augmentation technique to enhance the
linguistic diversity of the training data, and secondly, via the application of
deep prefix tuning for dialect adaptation of wav2vec2.0 models. In both tracks,
our approach yielded significant improvements over the provided baselines,
achieving the lowest word error rates across all participating teams
A Survey of Corpora for Germanic Low-Resource Languages and Dialects
Despite much progress in recent years, the vast majority of work in natural language processing (NLP) is on standard languages with many speakers. In this work, we instead focus on low-resource languages and in particular non-standardized low-resource languages. Even within branches of major language families, often considered well-researched, little is known about the extent and type of available resources and what the major NLP challenges are for these language varieties. The first step to address this situation is a systematic survey of available corpora (most importantly, annotated corpora, which are particularly valuable for NLP research). Focusing on Germanic low-resource language varieties, we provide such a survey in this paper. Except for geolocation (origin of speaker or document), we find that manually annotated linguistic resources are sparse and, if they exist, mostly cover morphosyntax. Despite this lack of resources, we observe that interest in this area is increasing: there is active development and a growing research community. To facilitate research, we make our overview of over 80 corpora publicly available