Search Results for author:
Found 8 papers, 5 papers with code
Utilizing Self-supervised Representations for MOS Prediction
In this paper, we use self-supervised pre-trained models for MOS prediction.
S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations
AUTOVC used dvector to extract speaker information, and self-supervised learning (SSL) features like wav2vec 2. 0 is used in FragmentVC to extract the phonetic content information.
SUPERB: Speech processing Universal PERformance Benchmark
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention
Any-to-any voice conversion aims to convert the voice from and to any speakers even unseen during training, which is much more challenging compared to one-to-one or many-to-many tasks, but much more attractive in real-world scenarios.
Defending Your Voice: Adversarial Attack on Voice Conversion
We introduce human imperceptible noise into the utterances of a speaker whose voice is to be defended.
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition
One of limitations in end-to-end automatic speech recognition (ASR) framework is its performance would be compromised if train-test utterance lengths are mismatched.
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition
In this paper, we improve the frame-level classifier for word timings in E2E system by introducing label priors in connectionist temporal classification (CTC) loss, which is adopted from prior works, and combining low-level Mel-scale filter banks with high-level ASR encoder output as input feature.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Large-Scale Evaluation of Speech Foundation Models
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
