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Found 10 papers, 1 papers with code
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
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models
We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition
In experiments using an attention-based encoder-decoder ASR system, we confirmed that ISF using the PBLM shows comparable performance with SF using the FLM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
NTT speaker diarization system for CHiME-7: multi-domain, multi-microphone End-to-end and vector clustering diarization
This paper details our speaker diarization system designed for multi-domain, multi-microphone casual conversations.
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.
Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data
Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture.
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods.
Leveraging Large Text Corpora for End-to-End Speech Summarization
The first technique is to utilize a text-to-speech (TTS) system to generate synthesized speech, which is used for E2E SSum training with the text summary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility
Many subjective experiments have been performed to develop objective speech intelligibility measures, but the novel coronavirus outbreak has made it very difficult to conduct experiments in a laboratory.
