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Found 10 papers, 3 papers with code
A Refining Underlying Information Framework for Monaural Speech Enhancement
By bridging the speech enhancement and the Information Bottleneck principle in this letter, we rethink a universal plug-and-play strategy and propose a Refining Underlying Information framework called RUI to rise to the challenges both in theory and practice.
On decoder-only architecture for speech-to-text and large language model integration
Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language.
VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation
Recent research shows a big convergence in model architecture, training objectives, and inference methods across various tasks for different modalities.
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement
In recent years, self-supervised learning (SSL) has achieved tremendous success in various speech tasks due to its power to extract representations from massive unlabeled data.
A CTC Triggered Siamese Network with Spatial-Temporal Dropout for Speech Recognition
Analysis shows that our proposed approach brings a better uniformity for the trained model and enlarges the CTC spikes obviously.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Multiple Confidence Gates For Joint Training Of SE And ASR
Joint training of speech enhancement model (SE) and speech recognition model (ASR) is a common solution for robust ASR in noisy environments.
Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE
Therefore, we previously proposed a harmonic gated compensation network (HGCN) to predict the full harmonic locations based on the unmasked harmonics and process the result of a coarse enhancement module to recover the masked harmonics.
HGCN: Harmonic gated compensation network for speech enhancement
Mask processing in the time-frequency (T-F) domain through the neural network has been one of the mainstreams for single-channel speech enhancement.
A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement
Deep learning technology has been widely applied to speech enhancement.
Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance
We find that using imaging techniques to input data for CNN works better for financial ratio data but is not significantly better than simply using the 1D input directly for fundamental data.
