Search Results for author:
Found 4 papers, 1 papers with code
SparseVSR: Lightweight and Noise Robust Visual Speech Recognition
We evaluate our 50% sparse model on 7 different visual noise types and achieve an overall absolute improvement of more than 2% WER compared to the dense equivalent.
Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels
Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets.
Ranked #1 on
Automatic Speech Recognition (ASR)
on LRS3-TED
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Automatic Viseme Vocabulary Construction to Enhance Continuous Lip-reading
Our results indicate that we are able to recognize approximately 58% of the visemes, 47% of the phonemes and 23% of the words in a continuous speech scenario and that the optimal viseme vocabulary for Spanish is composed by 20 visemes.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Towards Estimating the Upper Bound of Visual-Speech Recognition: The Visual Lip-Reading Feasibility Database
On one hand, researchers have reported that the mapping between phonemes and visemes (visual units) is one-to-many because there are phonemes which are visually similar and indistinguishable between them.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
