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Found 7 papers, 0 papers with code
Learning to Maximize Speech Quality Directly Using MOS Prediction for Neural Text-to-Speech
Although recent neural text-to-speech (TTS) systems have achieved high-quality speech synthesis, there are cases where a TTS system generates low-quality speech, mainly caused by limited training data or information loss during knowledge distillation.
A Unified Deep Learning Framework for Short-Duration Speaker Verification in Adverse Environments
At the same time, there is an increasing requirement for an SV system: it should be robust to short speech segments, especially in noisy and reverberant environments.
Deep MOS Predictor for Synthetic Speech Using Cluster-Based Modeling
While deep learning has made impressive progress in speech synthesis and voice conversion, the assessment of the synthesized speech is still carried out by human participants.
Neural MOS Prediction for Synthesized Speech Using Multi-Task Learning With Spoofing Detection and Spoofing Type Classification
In this paper, we propose a multi-task learning (MTL) method to improve the performance of a MOS prediction model using the following two auxiliary tasks: spoofing detection (SD) and spoofing type classification (STC).
Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances
In this approach, we obtain a speaker embedding vector by pooling single-scale features that are extracted from the last layer of a speaker feature extractor.
Self-Adaptive Soft Voice Activity Detection using Deep Neural Networks for Robust Speaker Verification
The first approach is soft VAD, which performs a soft selection of frame-level features extracted from a speaker feature extractor.
Spatial Pyramid Encoding with Convex Length Normalization for Text-Independent Speaker Verification
Furthermore, we apply deep length normalization by augmenting the loss function with ring loss.
