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Found 13 papers, 3 papers with code
FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech Self-Supervised Learning
Our method reduces the model to 23. 8% in size and 35. 9% in inference time compared to HuBERT.
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).
Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention
Keyword spotting (KWS) and speaker verification (SV) have been studied independently although it is known that acoustic and speaker domains are complementary.
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.
Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs
By combining these two learning schemes, our model outperforms existing state-of-the-art speaker verification models learned with a standard supervised learning framework on short utterance (1-2 seconds) on the VoxCeleb datasets.
Dual Attention in Time and Frequency Domain for Voice Activity Detection
The results show that the focal loss can improve the performance in various imbalance situations compared to the cross entropy loss, a commonly used loss function in VAD.
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings
Acoustic word embeddings --- fixed-dimensional vector representations of arbitrary-length words --- have attracted increasing interest in query-by-example spoken term detection.
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.
Learning acoustic word embeddings with phonetically associated triplet network
Previous researches on acoustic word embeddings used in query-by-example spoken term detection have shown remarkable performance improvements when using a triplet network.
