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Found 8 papers, 3 papers with code
RCT: Random Consistency Training for Semi-supervised Sound Event Detection
Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency.
Ranked #4 on
Sound Event Detection
on DESED
Acoustic Model Adaptation from Raw Waveforms with SincNet
Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a given scenario than hand-crafted features.
Stochastic Attention Head Removal: A simple and effective method for improving Transformer Based ASR Models
To the best of our knowledge, we have achieved state-of-the-art end-to-end Transformer based model performance on Switchboard and AMI.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
When Can Self-Attention Be Replaced by Feed Forward Layers?
Recently, self-attention models such as Transformers have given competitive results compared to recurrent neural network systems in speech recognition.
On the Usefulness of Self-Attention for Automatic Speech Recognition with Transformers
Self-attention models such as Transformers, which can capture temporal relationships without being limited by the distance between events, have given competitive speech recognition results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers
Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Top-down training for neural networks
Interpreting the top layers as a classifier and the lower layers a feature extractor, one can hypothesize that unwanted network convergence may occur when the classifier has overfit with respect to the feature extractor.
Zero-shot Audio Topic Reranking using Large Language Models
A key element for this process is highly rapid, flexible, search to support large archives, which in MVSE is facilitated by representing video attributes by embeddings.
