Mar 31, 2016 · The model is based on a unified neural network where the output of one task is fed to the input of the other, leading to a multi-task recurrent ...
The model is based on a unified neural network where the output of one task is fed to the input of the other, leading to a multi-task recurrent network.
A unified model to perform speech and speaker recognition simultaneously and altogether is presented, based on a unified neural network where the output of ...
The model is based on a multi-task recurrent neural network where the output of one task is fed as the input of the other, leading to a collaborative learning ...
Dec 13, 2016 · Through a comprehensive study, it is shown that the multitask recurrent neural net models deliver improved performance on both automatic speech ...
This paper presents a multi-task recurrent model that involves a multilingual ASR component and a language identification. (LID) component, and the ASR ...
This paper presents a multi-task recurrent model that involves a multilingual speech recognition (ASR) component and a language recognition (LR) component ...
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We propose a multi-task approach for speech and speaker recognition. A speech command and speaker recognition dataset in an industrial context.
The authors' multi-task learning networks can produce a shared speaker and speech embedding, which are evaluated on the LibriSpeech and VoxCeleb test sets, ...
We study multi-task learning for two orthogonal speech technology tasks: speech and speaker recognition. We use wav2vec2 as a base architecture with two ...