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Found 5 papers, 3 papers with code
Fast and parallel decoding for transducer
In this work, we introduce a constrained version of transducer loss to learn strictly monotonic alignments between the sequences; we also improve the standard greedy search and beam search algorithms by limiting the number of symbols that can be emitted per time step in transducer decoding, making it more efficient to decode in parallel with batches.
Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation
Although on-the-fly teacher label generation tackles this issue, the training speed is significantly slower as the teacher model has to be evaluated every batch.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Pruned RNN-T for fast, memory-efficient ASR training
The RNN-Transducer (RNN-T) framework for speech recognition has been growing in popularity, particularly for deployed real-time ASR systems, because it combines high accuracy with naturally streaming recognition.
Synchronous Bidirectional Learning for Multilingual Lip Reading
Based on this idea, we try to explore the synergized learning of multilingual lip reading in this paper, and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading
On the one hand, we introduce the evaluation metric (refers to the character error rate in this paper) as a form of reward to optimize the model together with the original discriminative target.
Ranked #9 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
