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Found 9 papers, 3 papers with code
Phonetic Error Analysis of Raw Waveform Acoustic Models with Parametric and Non-Parametric CNNs
Our raw waveform acoustic models consists of parametric (Sinc2Net) or non-parametric CNNs and Bidirectional LSTMs, achieving down to 13. 7%/15. 2% PERs on TIMIT Dev/Test sets, outperforming reported PERs for raw waveform models in the literature.
Zero-shot Audio Topic Reranking using Large Language Models
To gain a deeper understanding and further insights into the performance differences and limitations of these text sources, we employ a fact-checking approach to analyse the information consistency among them.
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 #5 on
Sound Event Detection
on DESED
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
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
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.
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.
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.
