Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling

09/06/2020
by   Songxiang Liu, et al.
0

This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq) based, non-parallel voice conversion approach. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq based synthesis module. During the training stage, an encoder-decoder based hybrid connectionist-temporal-classification-attention (CTC-attention) phoneme recognizer is trained, whose encoder has a bottle-neck layer. A BNE is obtained from the phoneme recognizer and is utilized to extract speaker-independent, dense and rich linguistic representations from spectral features. Then a multi-speaker location-relative attention based seq2seq synthesis model is trained to reconstruct spectral features from the bottle-neck features, conditioning on speaker representations for speaker identity control in the generated speech. To mitigate the difficulties of using seq2seq based models to align long sequences, we down-sample the input spectral feature along the temporal dimension and equip the synthesis model with a discretized mixture of logistic (MoL) attention mechanism. Since the phoneme recognizer is trained with large speech recognition data corpus, the proposed approach can conduct any-to-many voice conversion. Objective and subjective evaluations shows that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity. Ablation studies are conducted to confirm the effectiveness of feature selection and model design strategies in the proposed approach. The proposed VC approach can readily be extended to support any-to-any VC (also known as one/few-shot VC), and achieve high performance according to objective and subjective evaluations.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

09/30/2020

Transfer Learning from Speech Synthesis to Voice Conversion with Non-Parallel Training Data

This paper presents a novel framework to build a voice conversion (VC) s...
03/29/2019

Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet

We investigated the training of a shared model for both text-to-speech (...
06/25/2019

Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations

In this paper, a method for non-parallel sequence-to-sequence (seq2seq) ...
02/18/2022

VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion

Though significant progress has been made for speaker-dependent Video-to...
06/18/2020

Adversarially Trained Multi-Singer Sequence-To-Sequence Singing Synthesizer

This paper presents a high quality singing synthesizer that is able to m...
10/13/2016

Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder

We propose a flexible framework for spectral conversion (SC) that facili...
08/13/2020

Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion

The increased adoption of digital assistants makes text-to-speech (TTS) ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.