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Found 6 papers, 0 papers with code
Generative Context-aware Fine-tuning of Self-supervised Speech Models
Considering the recent advances in generative large language models (LLM), we hypothesize that an LLM could generate useful context information using the preceding text.
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points
In this paper, a novel sign-exponent-only floating-point network (SEOFP-NET) technique is proposed to compress the model size and accelerate the inference time for speech enhancement, a regression task of speech signal processing.
Efficient Inference For Neural Machine Translation
Large Transformer models have achieved state-of-the-art results in neural machine translation and have become standard in the field.
Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus
Machine learning has shown promise for automatic detection of Alzheimer's disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English.
Robustness against the channel effect in pathological voice detection
In this study, we propose a detection system for pathological voice, which is robust against the channel effect.
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)
We evaluated the proposed EOFP quantization technique on two types of neural networks, namely, bidirectional long short-term memory (BLSTM) and fully convolutional neural network (FCN), on a speech enhancement task.
