Arobert: An asr robust pre-trained language model for spoken language understanding
Spoken Language Understanding (SLU) aims to interpret the meanings of human speeches
in order to support various human-machine interaction systems. A key technique for SLU is …
in order to support various human-machine interaction systems. A key technique for SLU is …
Neural model reprogramming with similarity based mapping for low-resource spoken command recognition
In this study, we propose a novel adversarial reprogramming (AR) approach for low-
resource spoken command recognition (SCR), and build an AR-SCR system. The AR …
resource spoken command recognition (SCR), and build an AR-SCR system. The AR …
FastICARL: Fast incremental classifier and representation learning with efficient budget allocation in audio sensing applications
Various incremental learning (IL) approaches have been proposed to help deep learning
models learn new tasks/classes continuously without forgetting what was learned previously …
models learn new tasks/classes continuously without forgetting what was learned previously …
FewJoint: few-shot learning for joint dialogue understanding
Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of
attention. In this paper, we focus on the FSL problem of dialogue understanding, which …
attention. In this paper, we focus on the FSL problem of dialogue understanding, which …
Multimodal approach for code-mixed speech sentiment classification
S Keshav, G Jyothish Lal, B Premjith - Advances in Signal Processing …, 2023 - Springer
Sentiment analysis is a natural language processing (NLP) technique used to classify a
statement into three polarities, namely, positive negative and neutral. Thus, speech …
statement into three polarities, namely, positive negative and neutral. Thus, speech …
On the efficiency of integrating self-supervised learning and meta-learning for user-defined few-shot keyword spotting
User-defined keyword spotting is a task to detect new spoken terms defined by users. This
can be viewed as a few-shot learning problem since it is unreasonable for users to define …
can be viewed as a few-shot learning problem since it is unreasonable for users to define …
Meta-forecasting by combining global deep representations with local adaptation
While classical time series forecasting considers individual time series in isolation, recent
advances based on deep learning showed that jointly learning from a large pool of related …
advances based on deep learning showed that jointly learning from a large pool of related …
Meta auxiliary learning for low-resource spoken language understanding
Y Gao, J Feng, C Deng, S Zhang - arXiv preprint arXiv:2206.12774, 2022 - arxiv.org
Spoken language understanding (SLU) treats automatic speech recognition (ASR) and
natural language understanding (NLU) as a unified task and usually suffers from data …
natural language understanding (NLU) as a unified task and usually suffers from data …
[PDF][PDF] A Meta-Learning Approach for User-Defined Spoken Term Classification with Varying Classes and Examples.
Recently we formulated a user-defined spoken term classification task as a few-shot
learning task and tackled the task using Model-Agnostic Meta-Learning (MAML) algorithm …
learning task and tackled the task using Model-Agnostic Meta-Learning (MAML) algorithm …