Results 21 to 30 of about 1,389,721 (276)
Improvement of Acoustic Models Fused with Lip Visual Information for Low-Resource Speech
Sensors, 2023 Endangered language generally has low-resource characteristics, as an immaterial cultural resource that cannot be renewed. Automatic speech recognition (ASR) is an effective means to protect this language.
Chongchong Yu +3 more
doaj +1 more source
Bayesian Models for Unit Discovery on a Very Low Resource Language [PDF]
, 2018 Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising results on artificial examples but still lack of in situ experiments. Our work
Besacier, Laurent +9 more
core +3 more sources
Low-Resource Language Modelling of South African Languages
, 2021 Language models are the foundation of current neural network-based models for natural language understanding and generation. However, research on the intrinsic performance of language models on African languages has been extremely limited, which is made more challenging by the lack of large or standardised training and evaluation sets that exist for ...
Mesham, Stuart +3 more
openaire +2 more sources
Data augmentation for low resource languages [PDF]
Interspeech 2014, 2014 Recently there has been interest in the approaches for training speech recognition systems for languages with limited resources. Under the IARPA Babel program such resources have been provided for a range of languages to support this research area.
Ragni, Anton +3 more
openaire +2 more sources
A Teacher-Student Framework for Zero-Resource Neural Machine Translation [PDF]
, 2017 While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains.
Chen, Yun +3 more
core +2 more sources
Multitask Learning for Low Resource Spoken Language Understanding
Interspeech 2022, 2022 We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sentiment classification. Our models, although being of modest size, show improvements over models trained end-to-end on intent classification.
Meeus, Quentin +2 more
openaire +2 more sources
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages [PDF]
, 2018 Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches.
Barnes, Jeremy +2 more
core +3 more sources
Low-Resource Languages Jailbreak GPT-4
, 2023 NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023.
Yong, Zheng-Xin +2 more
openaire +2 more sources
Capsule Networks for Low Resource Spoken Language Understanding [PDF]
Interspeech 2018, 2018 Submitted to INTERSPEECH ...
Renkens, Vincent, Van hamme, Hugo
openaire +3 more sources
Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
Applied Sciences, 2021 Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a ...
Chanhee Lee +5 more
doaj +1 more source