MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475701" target="_blank" >RIV/00216208:11320/23:10475701 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.21437/SSW.2023-8" target="_blank" >http://dx.doi.org/10.21437/SSW.2023-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21437/SSW.2023-8" target="_blank" >10.21437/SSW.2023-8</a>

Result language
angličtina
Original language name
MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module
Original language description
We present MooseNet, a trainable speech metric that predicts the listeners' Mean Opinion Score (MOS). We propose a novel approach where the Probabilistic Linear Discriminative Analysis (PLDA) generative model is used on top of an embedding obtained from a self-supervised learning (SSL) neural network (NN) model. We show that PLDA works well with a non-finetuned SSL model when trained only on 136 utterances (ca. one minute training time) and that PLDA consistently improves various neural MOS prediction models, even stateof-the-art models with task-specific fine-tuning. Our ablation study shows PLDA training superiority over SSL model finetuning in a low-resource scenario. We also improve SSL model fine-tuning using a convenient optimizer choice and additional contrastive and multi-task training objectives. The fine-tuned MooseNet NN with the PLDA module achieves the best results, surpassing the SSL baseline on the VoiceMOS Challenge data.
Czech name
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Czech description
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Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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