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• W3171005929 abstract "In this paper, we aim to improve the traditional bottleneck feature extraction under the low-resource scenario. We employ the factorized hierarchical variational autoencoder (FHVAE) to learn an unsupervised feature representation by encoding the linguistic-relevant information into latent variables. In order to obtain more significant latent variables, the attention mechanism is introduced into the encoders of FHVAE. In addition to the reconstruction decoder of FHVAE, the phonetic-aware decoder is introduced to backward transmit the phonemic information into the latent variables, enhancing the performance of feature representation learning. The idea of multi-task learning is used to organize the encoders of FHVAE, the reconstruction decoder of FHVAE and the phonetic-aware decoder into the training process. To demonstrate the effectiveness of the proposed method, the ABX discriminability and the language identification are evaluated on the ZeroSpeech 2017 and the LRE 2017 respectively. These experimental results shown that the learned feature representation outperforms traditional acoustic feature." @default.
• W3171005929 created "2021-06-22" @default.
• W3171005929 creator A5070595920 @default.
• W3171005929 date "2021-05-07" @default.
• W3171005929 modified "2023-09-26" @default.
• W3171005929 title "Unsupervised Feature Representation Learning using Sequence-to-sequence Autoencoder Architecture for Low-resource Language" @default.
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• W3171005929 doi "https://doi.org/10.1109/ccai50917.2021.9447504" @default.
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