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An overview of voice conversion and its challenges: From statistical modeling to deep learning
Speaker identity is one of the important characteristics of human speech. In voice
conversion, we change the speaker identity from one to another, while kee** the linguistic …
conversion, we change the speaker identity from one to another, while kee** the linguistic …
Bangla natural language processing: A comprehensive analysis of classical, machine learning, and deep learning-based methods
The Bangla language is the seventh most spoken language, with 265 million native and non-
native speakers worldwide. However, English is the predominant language for online …
native speakers worldwide. However, English is the predominant language for online …
One-shot voice conversion by separating speaker and content representations with instance normalization
Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-
target scenario in which a single model is trained to convert the input voice to many different …
target scenario in which a single model is trained to convert the input voice to many different …
Sequence-to-sequence acoustic modeling for voice conversion
In this paper, a neural network named sequence-to-sequence ConvErsion NeTwork
(SCENT) is presented for acoustic modeling in voice conversion. At training stage, a SCENT …
(SCENT) is presented for acoustic modeling in voice conversion. At training stage, a SCENT …
JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
R Sonobe, S Takamichi, H Saruwatari - arxiv preprint arxiv:1711.00354, 2017 - arxiv.org
Thanks to improvements in machine learning techniques including deep learning, a free
large-scale speech corpus that can be shared between academic institutions and …
large-scale speech corpus that can be shared between academic institutions and …
Non-parallel sequence-to-sequence voice conversion with disentangled linguistic and speaker representations
This article presents a method of sequence-to-sequence (seq2seq) voice conversion using
non-parallel training data. In this method, disentangled linguistic and speaker …
non-parallel training data. In this method, disentangled linguistic and speaker …
Multi-target voice conversion without parallel data by adversarially learning disentangled audio representations
Recently, cycle-consistent adversarial network (Cycle-GAN) has been successfully applied
to voice conversion to a different speaker without parallel data, although in those …
to voice conversion to a different speaker without parallel data, although in those …
Voice transformer network: Sequence-to-sequence voice conversion using transformer with text-to-speech pretraining
We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based
on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models …
on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models …
AttS2S-VC: Sequence-to-sequence voice conversion with attention and context preservation mechanisms
This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with
attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq …
attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq …
Introduction to voice presentation attack detection and recent advances
Over the past few years, significant progress has been made in the field of presentation
attack detection (PAD) for automatic speaker recognition (ASV). This includes the …
attack detection (PAD) for automatic speaker recognition (ASV). This includes the …