1 citations · 1 across the 4 of their papers we have counts for
7 papers
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR
Ruizhi Li, Gregory Sell, Hynek Hermansky
Performance degradation of an Automatic Speech Recognition (ASR) system is commonly observed when the test acoustic condition is different from training. Hence, it is essential to…
A practical two-stage training strategy for multi-stream end-to-end speech recognition
Ruizhi Li, Gregory Sell, Xiaofei Wang +2
The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion. Our previous study o…
Multi-Stream End-to-End Speech Recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi +3
Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end (E2E) Automatic Speech Recognition (ASR). The…
Performance Monitoring for End-to-End Speech Recognition
Ruizhi Li, Gregory Sell, Hynek Hermansky
Measuring performance of an automatic speech recognition (ASR) system without ground-truth could be beneficial in many scenarios, especially with data from unseen domains, where pe…
Exploring Methods for the Automatic Detection of Errors in Manual Transcription
Xiaofei Wang, Jinyi Yang, Ruizhi Li +2
Quality of data plays an important role in most deep learning tasks. In the speech community, transcription of speech recording is indispensable. Since the transcription is usually…
Multi-encoder multi-resolution framework for end-to-end speech recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi +3
Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end Automatic Speech Recognition (ASR). The joint…