Abstract / truncated to 115 words (read the full abstract)

Over the last few years, i-vectors have been the state-of-the-art technique in speaker and language recognition. Recent advances in Deep Learning (DL) technology have improved the quality of i-vectors but the DL techniques in use are computationally expensive and need speaker or/and phonetic labels for the background data, which are not easily accessible in practice. On the other hand, the lack of speaker-labeled background data makes a big performance gap, in speaker recognition, between two well-known cosine and Probabilistic Linear Discriminant Analysis (PLDA) i-vector scoring techniques. It has recently been a challenge how to fill this gap without speaker labels, which are expensive in practice. Although some unsupervised clustering techniques are proposed to estimate the ... toggle 12 keywords

deep learning speaker recognition language recognition i-vector deep neural network deep belief network restricted boltzmann machine relu variable relu i-vector backend speaker embedding nist i-vector challenge


Ghahabi, Omid
Universitat Politecnica de Catalunya
Publication Year
Upload Date
Dec. 14, 2018

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