A review of deep learning techniques for speech processing
The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …
learning. The use of multiple processing layers has enabled the creation of models capable …
Superb@ slt 2022: Challenge on generalization and efficiency of self-supervised speech representation learning
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised
speech representation for better performance, generalization, and efficiency. The challenge …
speech representation for better performance, generalization, and efficiency. The challenge …
QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning
This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS
quality with lower supervised data requirements via Vector-Quantized Self-Supervised …
quality with lower supervised data requirements via Vector-Quantized Self-Supervised …
Improving self-supervised learning model for audio spoofing detection with layer-conditioned embedding fusion
The application of voice recognition systems has increased by a great deal with technology.
This has allowed adversaries to falsely claim access to these systems by spoofing the …
This has allowed adversaries to falsely claim access to these systems by spoofing the …
CTCBERT: Advancing hidden-unit BERT with CTC objectives
In this work, we present a simple but effective method, CTCBERT, for advancing hidden-unit
BERT (HuBERT). HuBERT applies a frame-level cross-entropy (CE) loss, which is similar to …
BERT (HuBERT). HuBERT applies a frame-level cross-entropy (CE) loss, which is similar to …