Speech And Audio Processing In Adverse Environments
By Ulrike Goldschmidt. 2016
37 citations recorded by the source corpus.
Subfields: Speech & Audio
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
- Mirco Ravanelli
- Yoshua Bengio
- Maurizio Omologo
- Philemon Brakel
- Piergiorgio Svaizer
- Titouan Parcollet
- Shinji Watanabe
- Dmitriy Serdyuk
Citations in the corpus
- Light Gated Recurrent Units for Speech Recognition (2018, background): “This lack of robustness typically happens when facing challenging acoustic conditions [13], characterized by considerable levels of non-stationary noise and acoustic re-”
- The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, background): “Despite the growing interest towards DSR, current technologies still exhibit a significant lack of robustness and flexibility, since the adverse acoustic conditions originated by non-stationary noises and acoustic reverberation make speech recognition significantly more challenging [3].”
- A network of deep neural networks for Distant Speech Recognition (2017, background): “Experiments, conducted using different datasets, tasks and acoustic conditions, revealed that the proposed framework can overtake other competitive solutions, including recent joint training approaches.”
- Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, background): “…[4], speech dereverberation [5], speaker localization [6], acoustic event detection [7] as well as acoustic modeling [8, 9, 10, 11, 12], DSR still exhibits a lack of robustness, especially when adverse acoustic conditions originated by non-stationary noises and acoustic reverberation are met [13].”
- Batch-normalized joint training for DNN-based distant speech recognition (2016, background): “To counteract such adversities, even the most recent DSR systems [9] must rely on a combination of several interconnected technologies, including for instance speech enhancement [10], speech separation [11], acoustic event detection and classification [12, 13], speaker identification [14], speaker…”