Search Results for author:
Found 15 papers, 10 papers with code
Self-supervised Representation Learning for Speech Processing
Due to the growing popularity of SSL, and the shared mission of the areas in bringing speech and language technologies to more use cases with better quality and scaling the technologies for under-represented languages, we propose this tutorial to systematically survey the latest SSL techniques, tools, datasets, and performance achievement in speech processing.
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.
A Comparative Study of Self-supervised Speech Representation Based Voice Conversion
We present a large-scale comparative study of self-supervised speech representation (S3R)-based voice conversion (VC).
Investigating self-supervised learning for speech enhancement and separation
Speech enhancement and separation are two fundamental tasks for robust speech processing.
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities
In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
We empirically showed that DUAL yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
Automatic Speech Recognition (ASR)
+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.
