Search Results for author:
Found 11 papers, 1 papers with code
Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery
For those experiencing severe-to-profound sensorineural hearing loss, the cochlear implant (CI) is the preferred treatment.
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)
The accurate reconstruction of surgical scenes from surgical videos is critical for various applications, including intraoperative navigation and image-guided robotic surgery automation.
Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label
AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates.
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
A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling
Automatic speech recognition (ASR) systems used on smart phones or vehicles are usually required to process speech queries from very different domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving CTC-based speech recognition via knowledge transferring from pre-trained language models
Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2. 0 models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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 Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model
In our framework, the encoder is initialized with a pretrained AM (wav2vec2. 0).
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
Improving Speech Recognition Accuracy of Local POI Using Geographical Models
Secondly, a group of geo-specific language models (Geo-LMs) are integrated into our speech recognition system to improve recognition accuracy of long tail and homophone POI.
