Search Results for author:
Found 5 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.
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.
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition
We combine the methods of meta learning and freeze of model parameters, which makes the recognition performance more stable in different cases and the training faster about 20%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Attention-based End-to-End Models for Small-Footprint Keyword Spotting
In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system.
Attention-Based End-to-End Speech Recognition on Voice Search
Previous attempts have shown that applying attention-based encoder-decoder to Mandarin speech recognition was quite difficult due to the logographic orthography of Mandarin, the large vocabulary and the conditional dependency of the attention model.
