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Found 14 papers, 3 papers with code
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding
In this work, we investigate impacts of this ASR error propagation on state-of-the-art NLU systems based on pre-trained language models (PLM), such as BERT and RoBERTa.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization
We further propose a sign-scale decomposition design to enhance training efficiency and a low-variance random initialization strategy to improve the model's transfer learning performance.
Low-bit Shift Network for End-to-End Spoken Language Understanding
Deep neural networks (DNN) have achieved impressive success in multiple domains.
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding
There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language.
JABER and SABER: Junior and Senior Arabic BERt
Language-specific pre-trained models have proven to be more accurate than multilingual ones in a monolingual evaluation setting, Arabic is no exception.
Speech-MLP: a simple MLP architecture for speech processing
Overparameterized transformer-based architectures have shown remarkable performance in recent years, achieving state-of-the-art results in speech processing tasks such as speech recognition, speech synthesis, keyword spotting, and speech enhancement et al.
A Streaming End-to-End Framework For Spoken Language Understanding
In this paper, we propose a streaming end-to-end framework that can process multiple intentions in an online and incremental way.
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation
End-to-end automatic speech recognition (ASR), unlike conventional ASR, does not have modules to learn the semantic representation from speech encoder.
Ranked #12 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Deep Label Distribution Learning with Label Ambiguity
However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.
Ranked #1 on
Head Pose Estimation
on BJUT-3D
Logistic Boosting Regression for Label Distribution Learning
In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint.
Chinese Song Iambics Generation with Neural Attention-based Model
Learning and generating Chinese poems is a charming yet challenging task.
Max-margin Metric Learning for Speaker Recognition
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
The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding.
