Speech and Audio Processing in Adverse Environments
By Eberhard Hnsler, G. Schmidt. 2008
63 citations recorded by the source corpus.
Subfields: Speech & Audio, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Speech & Audio
- Yoshua Bengio
- Mirco Ravanelli
- Maurizio Omologo
- Philemon Brakel
- Piergiorgio Svaizer
- Titouan Parcollet
- Shinji Watanabe
- Dmitriy Serdyuk
In Signal Processing
- Mirco Ravanelli
- Yoshua Bengio
- Maurizio Omologo
- Philemon Brakel
- Piergiorgio Svaizer
- Titouan Parcollet
- Santiago Pascual
- Aaron C. Courville
Citations in the corpus
- Light Gated Recurrent Units for Speech Recognition (2018, background): “ade, state-of-the-art speech recognizers are still far away from reaching satisfactory robustness and flexibility. This lack of robustness typically happens when facing challenging acoustic conditions [13], characterized by considerable levels of non-stationary noise and acoustic reverberation [14]–[”
- Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, methodology): “ell as acoustic modeling [8, 9, 10, 11, 12], DSR still exhibits a lack of robustness, especially when adverse acoustic conditions originated by nonstationary noises and acoustic reverberation are met [13]. The availability of realistic simulated corpora and, more importantly, the definition of common”
- Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, background): “rtunately, current DSR technologies still exhibit a significant lack of robustness and flexibility, due to the adverse acoustic conditions originated by non-stationary noises and acoustic reverberation [2]. Several efforts have been devoted during the last years to improve DSR, as also witnessed by so”