44 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
Globalisation - a threat to traditional landscapes and local identity?
Traditional landscapes could be considered as one of the most apparent carriers of local identities. They act as memory of previous human activities. During the 1990s the economic changes have had a drastic influence on the appearance of these landscapes. On one hand, local people are keen to keep the existing patterns that indicate their feeling of belonging. On the other hand, there is a desire to introduce new patterns dictated by new technologies, economic conditions, enlarged knowledge. This might lead to a kind of standardized landscape so that one cannot distinguish between, e.g., Denmark and western Estonia. The presentation will focus on the local identities in three Estonian counties. We deal with locals' preferences and ideas concerning their landscapes. Based on some 400 interviews we try to investigate which is the role of traditional landscape in local life, what kind of landscape changes are seen by locals as acceptable, what trade-offs are possible on landscape development, how could local people be involved in landscape planning, do current economic policies support or harm the maintenance of the traditional landscape.
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
Multilingual and Unsupervised Subword Modelingfor Zero-Resource Languages
Subword modeling for zero-resource languages aims to learn low-level
representations of speech audio without using transcriptions or other resources
from the target language (such as text corpora or pronunciation dictionaries).
A good representation should capture phonetic content and abstract away from
other types of variability, such as speaker differences and channel noise.
Previous work in this area has primarily focused unsupervised learning from
target language data only, and has been evaluated only intrinsically. Here we
directly compare multiple methods, including some that use only target language
speech data and some that use transcribed speech from other (non-target)
languages, and we evaluate using two intrinsic measures as well as on a
downstream unsupervised word segmentation and clustering task. We find that
combining two existing target-language-only methods yields better features than
either method alone. Nevertheless, even better results are obtained by
extracting target language bottleneck features using a model trained on other
languages. Cross-lingual training using just one other language is enough to
provide this benefit, but multilingual training helps even more. In addition to
these results, which hold across both intrinsic measures and the extrinsic
task, we discuss the qualitative differences between the different types of
learned features.Comment: 17 pages, 6 figures, 7 tables. Accepted for publication in Computer
Speech and Language. arXiv admin note: text overlap with arXiv:1803.0886