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

Follow @eurasip