Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …

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 …

摘要This paper introduces a new method to extract speaker embed-dings from a deep
neural network (DNN) for text-independent speaker verification. Usually, speaker …

We propose using self-supervised discrete representations for the task of speech
resynthesis. To generate disentangled representation, we separately extract low-bitrate …

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

Self-supervised learning in speech involves training a speech representation network on a
large-scale unannotated speech corpus, and then applying the learned representations to …

Deep learning is progressively gaining popularity as a viable alternative to i-vectors for
speaker recognition. Promising results have been recently obtained with Convolutional …

In this paper, we use data augmentation to improve performance of deep neural network
(DNN) embeddings for speaker recognition. The DNN, which is trained to discriminate …

Despite the progress in voice conversion, many-to-many voice conversion trained on non-
parallel data, as well as zero-shot voice conversion, remains under-explored. Deep style …

We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to
generate speech audio in the voice of many different speakers, including those unseen …