539 citations · 629 across the 10 of their papers we have counts for
10 papers
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding
Anderson R. Avila, Mehdi Rezagholizadeh, Chao Xing
Recent voice assistants are usually based on the cascade spoken language understanding (SLU) solution, which consists of an automatic speech recognition (ASR) engine and a natural…
Low-bit Shift Network for End-to-End Spoken Language Understanding
Anderson R. Avila, Khalil Bibi, Rui Heng Yang +3
Deep neural networks (DNN) have achieved impressive success in multiple domains. Over the years, the accuracy of these models has increased with the proliferation of deeper and mor…
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding
Abbas Ghaddar, Yimeng Wu, Sunyam Bagga +11
There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language. This work concerns addressing two major problems in existing…
JABER and SABER: Junior and Senior Arabic BERt
Abbas Ghaddar, Yimeng Wu, Ahmad Rashid +10
Language-specific pre-trained models have proven to be more accurate than multilingual ones in a monolingual evaluation setting, Arabic is no exception. However, we found that prev…
A Streaming End-to-End Framework For Spoken Language Understanding
Nihal Potdar, Anderson R. Avila, Chao Xing +3
End-to-end spoken language understanding (SLU) has recently attracted increasing interest. Compared to the conventional tandem-based approach that combines speech recognition and l…
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation
Md Akmal Haidar, Chao Xing, Mehdi Rezagholizadeh
End-to-end automatic speech recognition (ASR), unlike conventional ASR, does not have modules to learn the semantic representation from speech encoder. Moreover, the higher frame-r…
Deep Label Distribution Learning with Label Ambiguity
Bin-Bin Gao, Chao Xing, Chen-Wei Xie +2
Convolutional Neural Networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most import…
Chinese Song Iambics Generation with Neural Attention-based Model
Qixin Wang, Tianyi Luo, Dong Wang +1
Learning and generating Chinese poems is a charming yet challenging task. Traditional approaches involve various language modeling and machine translation techniques, however, they…
Max-margin Metric Learning for Speaker Recognition
Lantian Li, Dong Wang, Chao Xing +1
Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition.…
Binary Speaker Embedding
Lantian Li, Dong Wang, Chao Xing +2
The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding. In this paper, we investiga…