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Found 34 papers, 15 papers with code
A Large-Scale Evaluation of Speech Foundation Models
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023
This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023.
The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains
We present the second edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthesized and processed speech.
Evaluating Methods for Ground-Truth-Free Foreign Accent Conversion
In this work, we evaluate three recently proposed methods for ground-truth-free FAC, where all of them aim to harness the power of sequence-to-sequence (seq2seq) and non-parallel VC models to properly convert the accent and control the speaker identity.
The Singing Voice Conversion Challenge 2023
A new database was constructed for two tasks, namely in-domain and cross-domain SVC.
A Holistic Cascade System, benchmark, and Human Evaluation Protocol for Expressive Speech-to-Speech Translation
Expressive speech-to-speech translation (S2ST) aims to transfer prosodic attributes of source speech to target speech while maintaining translation accuracy.
A Comparative Study of Self-supervised Speech Representation Based Voice Conversion
We present a large-scale comparative study of self-supervised speech representation (S3R)-based voice conversion (VC).
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities
In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.
LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech
An effective approach to automatically predict the subjective rating for synthetic speech is to train on a listening test dataset with human-annotated scores.
Towards Identity Preserving Normal to Dysarthric Voice Conversion
We present a voice conversion framework that converts normal speech into dysarthric speech while preserving the speaker identity.
S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations
In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting.
Generalization Ability of MOS Prediction Networks
Automatic methods to predict listener opinions of synthesized speech remain elusive since listeners, systems being evaluated, characteristics of the speech, and even the instructions given and the rating scale all vary from test to test.
Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion
Voice conversion (VC) is an effective approach to electrolaryngeal (EL) speech enhancement, a task that aims to improve the quality of the artificial voice from an electrolarynx device.
On Prosody Modeling for ASR+TTS based Voice Conversion
In voice conversion (VC), an approach showing promising results in the latest voice conversion challenge (VCC) 2020 is to first use an automatic speech recognition (ASR) model to transcribe the source speech into the underlying linguistic contents; these are then used as input by a text-to-speech (TTS) system to generate the converted speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available.
A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion
First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a reference speaker as an intermediate product, and a nonparallel, frame-wise VC model realized with a variational autoencoder then converts the speaker identity of the reference speech back to that of the patient while assumed to be capable of preserving the enhanced quality.
Non-autoregressive sequence-to-sequence voice conversion
This paper proposes a novel voice conversion (VC) method based on non-autoregressive sequence-to-sequence (NAR-S2S) models.
The AS-NU System for the M2VoC Challenge
The first track focuses on using a small number of 100 target utterances for voice cloning, while the second track focuses on using only 5 target utterances for voice cloning.
crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder
In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank.
EMA2S: An End-to-End Multimodal Articulatory-to-Speech System
Synthesized speech from articulatory movements can have real-world use for patients with vocal cord disorders, situations requiring silent speech, or in high-noise environments.
Speech Recognition by Simply Fine-tuning BERT
We propose a simple method for automatic speech recognition (ASR) by fine-tuning BERT, which is a language model (LM) trained on large-scale unlabeled text data and can generate rich contextual representations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations
Given a training dataset of the target speaker, we extract VQW2V and acoustic features to estimate a seq2seq mapping function from the former to the latter.
The NU Voice Conversion System for the Voice Conversion Challenge 2020: On the Effectiveness of Sequence-to-sequence Models and Autoregressive Neural Vocoders
In this paper, we present the voice conversion (VC) systems developed at Nagoya University (NU) for the Voice Conversion Challenge 2020 (VCC2020).
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice conversion challenge (VCC) 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Many-to-Many Voice Transformer Network
The main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers.
Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion
In this paper, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech.
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.
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion
Such hypothesis implies that during the conversion phase, the latent codes and the converted features in VAE based VC are in fact source F0 dependent.
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion
Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation.
Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
An effective approach to non-parallel voice conversion (VC) is to utilize deep neural networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure of speech in an unsupervised manner.
