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Found 8 papers, 0 papers with code
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach
For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition
We present a novel sub-8-bit quantization-aware training (S8BQAT) scheme for 8-bit neural network accelerators.
Scalable and Efficient Neural Speech Coding: A Hybrid Design
We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine.
Sparsification via Compressed Sensing for Automatic Speech Recognition
In the literature, such methods are referred to as sparse pruning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding
Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded audio signals.
A Dual-Staged Context Aggregation Method Towards Efficient End-To-End Speech Enhancement
In speech enhancement, an end-to-end deep neural network converts a noisy speech signal to a clean speech directly in time domain without time-frequency transformation or mask estimation.
Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding
Speech codecs learn compact representations of speech signals to facilitate data transmission.
A Hybrid Supervised-unsupervised Method on Image Topic Visualization with Convolutional Neural Network and LDA
Given the progress in image recognition with recent data driven paradigms, it's still expensive to manually label a large training data to fit a convolutional neural network (CNN) model.
