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Found 8 papers, 1 papers with code
An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task Learning
Lastly, we show that the proposed recipe can be applied to other distillation methodologies, such as the recent DPWavLM.
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''
Recent advances with self-supervised learning have allowed speech recognition systems to achieve state-of-the-art (SOTA) word error rates (WER) while requiring only a fraction of the labeled training data needed by its predecessors.
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
RobustDistiller: Compressing Universal Speech Representations for Enhanced Environment Robustness
The proposed layer-wise distillation recipe is evaluated on top of three well-established universal representations, as well as with three downstream tasks.
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement
Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech and/or emotion recognition.
Low-bit Shift Network for End-to-End Spoken Language Understanding
Deep neural networks (DNN) have achieved impressive success in multiple domains.
Sequential End-to-End Intent and Slot Label Classification and Localization
Such approaches allow for the extraction of semantic information directly from the speech signal, thus bypassing the need for a transcript from an automatic speech recognition (ASR) system.
Ranked #11 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
