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 keeping the linguistic …
conversion, we change the speaker identity from one to another, while keeping the linguistic …
[PDF][PDF] Speech synthesis evaluation—state-of-the-art assessment and suggestion for a novel research program
Speech synthesis applications have become an ubiquity, in navigation systems, digital
assistants or as screen or audio book readers. Despite their impact on the acceptability of …
assistants or as screen or audio book readers. Despite their impact on the acceptability of …
The voicemos challenge 2022
We present the first edition of the VoiceMOS Challenge, a scientific event that aims to
promote the study of automatic prediction of the mean opinion score (MOS) of synthetic …
promote the study of automatic prediction of the mean opinion score (MOS) of synthetic …
Mosnet: Deep learning based objective assessment for voice conversion
Existing objective evaluation metrics for voice conversion (VC) are not always correlated
with human perception. Therefore, training VC models with such criteria may not effectively …
with human perception. Therefore, training VC models with such criteria may not effectively …
Utmos: Utokyo-sarulab system for voicemos challenge 2022
We present the UTokyo-SaruLab mean opinion score (MOS) prediction system submitted to
VoiceMOS Challenge 2022. The challenge is to predict the MOS values of speech samples …
VoiceMOS Challenge 2022. The challenge is to predict the MOS values of speech samples …
MBNet: MOS prediction for synthesized speech with mean-bias network
Mean opinion score (MOS) is a popular subjective metric to assess the quality of
synthesized speech, and usually involves multiple human judges to evaluate each speech …
synthesized speech, and usually involves multiple human judges to evaluate each speech …
CDPAM: Contrastive learning for perceptual audio similarity
Many speech processing methods based on deep learning require an automatic and
differentiable audio metric for the loss function. The DPAM approach of Manocha et al.[1] …
differentiable audio metric for the loss function. The DPAM approach of Manocha et al.[1] …
Deep learning based assessment of synthetic speech naturalness
In this paper, we present a new objective prediction model for synthetic speech naturalness.
It can be used to evaluate Text-To-Speech or Voice Conversion systems and works …
It can be used to evaluate Text-To-Speech or Voice Conversion systems and works …
Utilizing self-supervised representations for MOS prediction
Speech quality assessment has been a critical issue in speech processing for decades.
Existing automatic evaluations usually require clean references or parallel ground truth data …
Existing automatic evaluations usually require clean references or parallel ground truth data …
NORESQA: A framework for speech quality assessment using non-matching references
The perceptual task of speech quality assessment (SQA) is a challenging task for machines
to do. Objective SQA methods that rely on the availability of the corresponding clean …
to do. Objective SQA methods that rely on the availability of the corresponding clean …