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Found 12 papers, 1 papers with code
Deploying self-supervised learning in the wild for hybrid automatic speech recognition
Self-supervised learning (SSL) methods have proven to be very successful in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Sequence-level self-learning with multiple hypotheses
For untranscribed speech data, the hypothesis from an ASR system must be used as a label.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Federated Learning is a fast growing area of ML where the training datasets are extremely distributed, all while dynamically changing over time.
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes
Multilingual end-to-end(E2E) models have shown a great potential in the expansion of the language coverage in the realm of automatic speech recognition(ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset
Recently, there has been a vast interest in self-supervised learning (SSL) where the model is pre-trained on large scale unlabeled data and then fine-tuned on a small labeled dataset.
Dynamic Gradient Aggregation for Federated Domain Adaptation
The proposed scheme is based on a weighted gradient aggregation using two-step optimization to offer a flexible training pipeline.
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner.
Federated Transfer Learning with Dynamic Gradient Aggregation
The target scenario is Acoustic Model training based on this platform.
Robust Multi-channel Speech Recognition using Frequency Aligned Network
In this paper, we further develop this idea and use frequency aligned network for robust multi-channel automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
Using a large offline teacher model trained on beamformed audio, we trained a simpler multi-channel student acoustic model used in the speech recognition system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning
For real-world speech recognition applications, noise robustness is still a challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
