Morphling: Fast, near-optimal auto-configuration for cloud-native model serving
Machine learning models are widely deployed in production cloud to provide online
inference services. Efficiently deploying inference services requires careful tuning of …
inference services. Efficiently deploying inference services requires careful tuning of …
Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer
Breast cancer is common in women, and its number of patients ranks first among female
malignant tumors. Breast cancer is highly heterogeneous, and different types of breast …
malignant tumors. Breast cancer is highly heterogeneous, and different types of breast …
Recurrent neural network language model training with noise contrastive estimation for speech recognition
In recent years recurrent neural network language models (RNNLMs) have been
successfully applied to a range of tasks including speech recognition. However, an …
successfully applied to a range of tasks including speech recognition. However, an …
Efficient training and evaluation of recurrent neural network language models for automatic speech recognition
Recurrent neural network language models (RNNLMs) are becoming increasingly popular
for a range of applications including automatic speech recognition. An important issue that …
for a range of applications including automatic speech recognition. An important issue that …
Artificial neural networks combined with the principal component analysis for non-fluent speech recognition
I Świetlicka, W Kuniszyk-Jóźkowiak, M Świetlicki - Sensors, 2022 - mdpi.com
The presented paper introduces principal component analysis application for dimensionality
reduction of variables describing speech signal and applicability of obtained results for the …
reduction of variables describing speech signal and applicability of obtained results for the …
Live streaming speech recognition using deep bidirectional LSTM acoustic models and interpolated language models
Although Long-Short Term Memory (LSTM) networks and deep Transformers are now
extensively used in offline ASR, it is unclear how best offline systems can be adapted to …
extensively used in offline ASR, it is unclear how best offline systems can be adapted to …
[PDF][PDF] Real-Time One-Pass Decoder for Speech Recognition Using LSTM Language Models.
Abstract Recurrent Neural Networks, in particular Long-Short Term Memory (LSTM)
networks, are widely used in Automatic Speech Recognition for language modelling during …
networks, are widely used in Automatic Speech Recognition for language modelling during …
A parallelizable lattice rescoring strategy with neural language models
This paper proposes a parallel computation strategy and a posterior-based lattice expansion
algorithm for efficient lattice rescoring with neural language models (LMs) for automatic …
algorithm for efficient lattice rescoring with neural language models (LMs) for automatic …
Improving the training and evaluation efficiency of recurrent neural network language models
Recurrent neural network language models (RNNLMs) are becoming increasingly popular
for speech recognition. Previously, we have shown that RNNLMs with a full (non-classed) …
for speech recognition. Previously, we have shown that RNNLMs with a full (non-classed) …
[HTML][HTML] Streaming cascade-based speech translation leveraged by a direct segmentation model
J Iranzo-Sánchez, J Jorge, P Baquero-Arnal… - Neural Networks, 2021 - Elsevier
The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates
an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) …
an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) …