Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

Significant memory and computational requirements of large deep neural networks restrict
their application on edge devices. Knowledge distillation (KD) is a prominent model …

Neural network quantization is a popular approach for model compression. Modern
hardware supports quantization in mixed-precision mode, which allows for greater …

With the rapid adoption of machine learning (ML), a number of domains now use the
approach of fine tuning models which were pre-trained on a large corpus of data. However …

Reducing the latency and model size has always been a significant research problem for
live Automatic Speech Recognition (ASR) application scenarios. Along this direction, model …

End-to-end automatic speech recognition (ASR) models have seen revolutionary quality
gains with the recent development of large-scale universal speech models (USM). However …

End-to-end neural network models achieve improved performance on various automatic
speech recognition (ASR) tasks. However, these models perform poorly on edge hardware …

With the development of deep learning, neural networks are widely used in various fields,
and the improved model performance also introduces a considerable number of parameters …

This paper presents a dialect identification (DID) system based on the transformer neural
network architecture. The conventional convolutional neural network (CNN)-based systems …