In this paper, we address the problem of speaker verification in conditions unseen or
unknown during development. A standard method for speaker verification consists of …

Probabilistic linear discriminant analysis (PLDA) is widely implemented in speaker
verification tasks. However, PLDA has limitations owing to its assumptions. In this study, we …

The deep learning methodologies in state-of-the-art speaker recognition systems are
predominantly limited to the extraction of recording level embeddings. This is usually …

Verifying the speaker of a speech fragment can be crucial in attributing a crime to a suspect.
The question can be addressed given disputed and reference speech material, adopting the …

Conventional automatic speaker verification systems can usually be decomposed into a
front-end model such as time delay neural network (TDNN) for extracting speaker …

Currently, most speaker recognition backends, such as cosine, linear discriminant analysis
(LDA), or probabilistic linear discriminant analysis (PLDA), make decisions by calculating …

In the age of artificial intelligence, it is important for machines to figure out who is speaking
automatically and in what language-a task humans are naturally capable of. Developing …

The task of speaker verification (SV) is to decide whether an utterance is spoken by a target
or an imposter speaker. In most studies of SV, a log-likelihood ratio (LLR) score is estimated …

分解することでパラメタ数を減らした factorized TDNN [6] などが提案されている. また, TDNN
の各層を階層的な残差 (residual-like connection in residual; Res2) ブロックと SE (squeeze …

Automatic speaker verification (ASV) has shown immense potential across domains like
security, forensic analysis, and human-computer interaction. However, real-world …