This paper presents a new stochastic learning approach to construct a latent variable model for recurrent neural network (RNN) based speech recognition.
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Stochastic Recurrent Neural Network for Speech Recognition. August 2017. DOI:10.21437/Interspeech.2017-856. Conference: Interspeech 2017. Authors: Jen-Tzung ...
In this paper, a novel optimization method - stochastic natural gradient based on minimum variance assumption (SNGM) is proposed for training RNNLMs. It allows ...
This paper presents a new stochastic learning approach to construct a latent variable model for recurrent neural network (RNN) based speech recognition.
May 25, 2021 · A novel system for effective speech recognition based on artificial neural network and opposition artificial bee colony algorithm.
Apr 1, 2023 · Leveraging these tricks, this article proposes an automatic speech recognition model with a stacked five layers of customized Residual ...
Given that speech is an inherently dynamic process, it seems natural to consider recurrent neu- ral networks (RNNs) as an alternative model. HMM-RNN systems [5] ...
Missing: Stochastic | Show results with:Stochastic
Speech recognition with deep recurrent neural networks. In Acoustics, Speech and Signal Process- ing (ICASSP), 2013 IEEE International Conference on, pages ...
Feb 22, 2024 · Our results suggest that RNNs provide plausible computational models of the cortical processes supporting human speech recognition. Introduction.
In this paper we consider the training stability of recurrent neural networks (RNNs), and propose a family of RNNs, namely SBO-RNN, that can be formulated ...