Search Results for author:
Found 6 papers, 1 papers with code
Using Spoken Word Posterior Features in Neural Machine Translation
In this paper, a neural sequence-to-sequence ASR is used as feature processing that is trained to produce word posterior features given spoken utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition
Confidence estimation, in which we estimate the reliability of each recognized token (e. g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and detect incorrectly recognized tokens, is an important function for developing ASR applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization
End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model.
Attention-based Multi-hypothesis Fusion for Speech Summarization
We propose a cascade speech summarization model that is robust to ASR errors and that exploits multiple hypotheses generated by ASR to attenuate the effect of ASR errors on the summary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS
This paper presents a newly developed, simultaneous neural speech-to-speech translation system and its evaluation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation
Sequence-to-sequence attentional-based neural network architectures have been shown to provide a powerful model for machine translation and speech recognition.
