Eurospeech 2001-Scandinavia Robust Speech Recognition in Noise : An Evaluation using the SPINE Corpus †
By John H. L. Hansen, Ruhi Sarikaya, Umit Yapanel, B. Pellom. 2001
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Arsha Nagrani
- Andrew Zisserman
- Joon Son Chung
- Andrea Vedaldi
- Weidi Xie
- Kristen Grauman
- Yoshua Bengio
- Xiaogang Wang
Citations in the corpus
- VoxCeleb: A Large-Scale Speaker Identification Dataset (2017, background): “However, not only does the performance of MFCCs degrade rapidly in real world noise [11, 12], but by focusing only on the overall spectral envelope of short frames, MFCCs may be lacking in speaker-discriminating features (such as pitch information).”
- Vggsound: A Large-Scale Audio-Visual Dataset (2020, background): “However, the performance of MFCCs in audio recognition degrades rapidly in “unconstrained” environments that include real-world noise [25, 26].”
- Voxceleb: Large-scale speaker verification in the wild (2020, background): “However, not only does the performance of MFCCs degrade rapidly in real-world noise (Yapanel et al., 2002; Hansen et al., 2001), but by focusing only on the overall spectral envelope of short frames, MFCCs may be lacking in speaker-discriminating features (such as pitch information).”