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Found 13 papers, 2 papers with code
Skipformer: A Skip-and-Recover Strategy for Efficient Speech Recognition
Conformer-based attention models have become the de facto backbone model for Automatic Speech Recognition tasks.
Self-Supervised Disentangled Representation Learning for Robust Target Speech Extraction
However, in the task of target speech extraction, certain elements of global and local semantic information in the reference speech, which are irrelevant to speaker identity, can lead to speaker confusion within the speech extraction network.
Key Frame Mechanism For Efficient Conformer Based End-to-end Speech Recognition
Following the initial encoder, we introduce an intermediate CTC loss function to compute the label frame, enabling us to extract the key frames and blank frames for KFSA.
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting
Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system.
Two Stage Contextual Word Filtering for Context bias in Unified Streaming and Non-streaming Transducer
In this paper, we propose an efficient approach to obtain a high quality contextual list for a unified streaming/non-streaming based E2E model.
Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR
Then, during the training of the conversational ASR system, the extractor will be frozen to extract the textual representation of preceding speech, while such representation is used as context fed to the ASR decoder through attention mechanism.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Conversational Speech Recognition By Learning Conversation-level Characteristics
Conversational automatic speech recognition (ASR) is a task to recognize conversational speech including multiple speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning
Under such a framework, the neural network is usually pre-trained with massive unlabeled data and then fine-tuned with limited labeled data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-head Monotonic Chunkwise Attention For Online Speech Recognition
Experiments on AISHELL-1 data show that the proposed model, along with the training strategies, improve the character error rate (CER) of MoChA from 8. 96% to 7. 68% on test set.
Training Augmentation with Adversarial Examples for Robust Speech Recognition
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models.
Domain Adversarial Training for Accented Speech Recognition
In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem.
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition
First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset.
The NNI Query-by-Example System for MediaEval 2015
This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation.
Ranked #9 on
Keyword Spotting
on QUESST
