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• W2970407246 abstract "Deep speaker embedding has achieved satisfactory performance in speaker verification. By enforcing the neural model to discriminate the speakers in the training set, deep speaker embedding (called `x-vectors`) can be derived from the hidden layers. Despite its good performance, the present embedding model is highly domain sensitive, which means that it often works well in domains whose acoustic condition matches that of the training data (in-domain), but degrades in mismatched domains (out-of-domain). In this paper, we present a domain adaptation approach based on Variational Auto-Encoder (VAE). This model transforms x-vectors to a regularized latent space; within this latent space, a small amount of data from the target domain is sufficient to accomplish the adaptation. Our experiments demonstrated that by this VAE-adaptation approach, speaker embeddings can be easily transformed to the target domain, leading to noticeable performance improvement." @default.
• W2970407246 created "2019-09-05" @default.
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• W2970407246 date "2019-08-27" @default.
• W2970407246 modified "2023-09-24" @default.
• W2970407246 title "VAE-based Domain Adaptation for Speaker Verification" @default.
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• W2970407246 doi "https://doi.org/10.48550/arxiv.1908.10092" @default.
• W2970407246 hasPublicationYear "2019" @default.
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