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Hits 1 – 6 of 6
1
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation ...
Chen, Yi-Chen
;
Hsu, Jui-Yang
;
Lee, Cheng-Kuang
. - : arXiv, 2020
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2
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings ...
Chen, Yi-Chen
;
Huang, Sung-Feng
;
Lee, Hung-yi
. - : arXiv, 2019
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3
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data ...
Chen, Yi-Chen
;
Shen, Chia-Hao
;
Huang, Sung-Feng
. - : arXiv, 2018
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4
Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval ...
Chen, Yi-Chen
;
Huang, Sung-Feng
;
Shen, Chia-Hao
. - : arXiv, 2018
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5
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only ...
Chen, Yi-Chen
;
Shen, Chia-Hao
;
Huang, Sung-Feng
. - : arXiv, 2018
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6
Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection ...
Huang, Sung-Feng
;
Chen, Yi-Chen
;
Lee, Hung-yi
;
Lee, Lin-shan
. - : arXiv, 2018
Abstract:
Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, classifying or indexing. Audio Word2Vec previously proposed was shown to be able to represent audio segments for spoken words as such vectors carrying information about the phonetic structures of the signal segments. However, each linguistic unit (word, syllable, phoneme in text form) corresponds to unlimited number of audio segments with vector representations inevitably spread over the embedding space, which causes some confusion. It is therefore desired to better cluster the audio embeddings such that those corresponding to the same linguistic unit can be more compactly distributed. In this paper, inspired by Siamese networks, we propose some approaches to achieve the above goal. This includes identifying positive and negative pairs from unlabeled data for Siamese style training, disentangling acoustic factors such as speaker ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://dx.doi.org/10.48550/arxiv.1811.02775
https://arxiv.org/abs/1811.02775
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