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Found 8 papers, 4 papers with code
Self-critical Sequence Training for Automatic Speech Recognition
Although automatic speech recognition (ASR) task has gained remarkable success by sequence-to-sequence models, there are two main mismatches between its training and testing that might lead to performance degradation: 1) The typically used cross-entropy criterion aims to maximize log-likelihood of the training data, while the performance is evaluated by word error rate (WER), not log-likelihood; 2) The teacher-forcing method leads to the dependence on ground truth during training, which means that model has never been exposed to its own prediction before testing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting
Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment.
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning
Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio.
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data
Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in face of various practical environments.
Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition
Then, we propose style learning to map the fused feature close to clean feature, in order to learn latent speech information from the latter, i. e., clean "speech style".
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Progressive Continual Learning for Spoken Keyword Spotting
Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment.
Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition
Speech enhancement (SE) aims to suppress the additive noise from a noisy speech signal to improve the speech's perceptual quality and intelligibility.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech
One of the advantage of the proposed method is that the entire system can be trained from scratch.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
