Search Results for author:
Found 9 papers, 7 papers with code
Speech Representation Learning Through Self-supervised Pretraining And Multi-task Finetuning
It has been shown that an SSL pretraining model can achieve excellent performance in various downstream tasks of speech processing.
S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations
In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting.
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition
We select several pretrained speech representations and present the experimental results on various open-source and publicly available corpora for E2E-ASR.
automatic-speech-recognition
End-To-End Speech Recognition
+1
DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT
Self-supervised speech representation learning methods like wav2vec 2. 0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks.
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
The multi-task learning of a wide variety of speech processing tasks with a universal model has not been studied.
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.
Understanding Self-Attention of Self-Supervised Audio Transformers
Self-supervised Audio Transformers (SAT) enable great success in many downstream speech applications like ASR, but how they work has not been widely explored yet.
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech.
DANE: Domain Adaptive Network Embedding
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.
