Impulse response estimation for robust speech recognition in a reverberant environment
By Mirco Ravanelli, Alessandro Sosi, Piergiorgio Svaizer, Maurizio Omologo. 2012
32 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
- Mirco Ravanelli
- Yoshua Bengio
- 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
- The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, methodology): “For more details on the creation of the ITEA IR database, please refer to [30, 18].”
- Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, methodology): “This method of measuring the IR has a remarkable impact on the speech recognition performance, as discussed in [29].”
- Learning Speaker Representations with Mutual Information (2018, methodology): “In this case, all the clean signals were convoluted with a different impulse response [35], that was sampled from the DIRHA dataset [36, 37].”
- Batch-normalized joint training for DNN-based distant speech recognition (2016, background): “More details about the data contamination approach can be found in [45, 46, 47].”
- Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, methodology): “In [33], a comparison between these different methods has been proposed for distant speech recognition purposes, showing that ESS out-performs the other methods, especially when long (1 minute) and high dynamic excitation signals can be emitted in the acoustic environment.”