5 citations · 5 across the 5 of their papers we have counts for
6 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…
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…
Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection
Sandeep Kothinti, Keisuke Imoto, Debmalya Chakrabarty +3
Sound event detection is a challenging task, especially for scenes with multiple simultaneous events. While event classification methods tend to be fairly accurate, event localizat…
Building Corpora for Single-Channel Speech Separation Across Multiple Domains
Matthew Maciejewski, Gregory Sell, Leibny Paola Garcia-Perera +2
To date, the bulk of research on single-channel speech separation has been conducted using clean, near-field, read speech, which is not representative of many modern applications.…
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks
Aren Jansen, Gregory Sell, Vince Lyzinski
Several popular graph embedding techniques for representation learning and dimensionality reduction rely on performing computationally expensive eigendecompositions to derive a non…