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Found 18 papers, 5 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).
i-Code: An Integrative and Composable Multimodal Learning Framework
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data
In this way, the decoder learns to reconstruct original speech information with codes before learning to generate correct text.
Self-Supervised Learning for speech recognition with Intermediate layer supervision
Recently, pioneer work finds that speech pre-trained models can solve full-stack speech processing tasks, because the model utilizes bottom layers to learn speaker-related information and top layers to encode content-related information.
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
The reconstruction module is used for auxiliary learning to improve the noise robustness of the learned representation and thus is not required during inference.
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks.
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).
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.
UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training
We integrate the proposed methods into the HuBERT framework.
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition
In this paper we propose wav2vec-Switch, a method to encode noise robustness into contextualized representations of speech via contrastive learning.
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.
Speech-language Pre-training for End-to-end Spoken Language Understanding
End-to-end (E2E) spoken language understanding (SLU) can infer semantics directly from speech signal without cascading an automatic speech recognizer (ASR) with a natural language understanding (NLU) module.
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.
To Trust, or Not to Trust? A Study of Human Bias in Automated Video Interview Assessments
Supervised systems require human labels for training.
A Report on the 2017 Native Language Identification Shared Task
We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks.
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e. g. speech utterances or handwritten documents.
Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e. g. speech utterances or handwritten documents.
