Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

Identifying a person by his or her voice is an important human trait most take for granted in
natural human-to-human interaction/communication. Speaking to someone over the …

The objective of this paper is speaker recognition under noisy and unconstrained conditions.
We make two key contributions. First, we introduce a very large-scale audio-visual speaker …

We present Deep Speaker, a neural speaker embedding system that maps utterances to a
hypersphere where speaker similarity is measured by cosine similarity. The embeddings …

We present a method to boost the performance of probabilistic generative models that work
with i-vector representations. The proposed approach deals with the non-Gaussian behavior …

Abstract Automatic Emotion Recognition (AER) is critical for naturalistic Human–Machine
Interactions (HMI). Emotions can be detected through both external behaviors, eg, tone of …

The i-vector approach to speaker recognition has achieved good performance when the
domain of the evaluation dataset is similar to that of the training dataset. However, in …

Recently deep learning has been successfully used in speech recognition, however it has
not been carefully explored and widely accepted for speaker verification. To incorporate …

The duration of speech segments has traditionally been controlled in the NIST speaker
recognition evaluations so that researchers working in this framework have been relieved of …

With the rapid growth of wearable computing and increasing demand for mobile
authentication scenarios, voiceprint-based authentication has become one of the prevalent …