Informational and energetic masking effects in the perception of two simultaneous talkers.
By D. Brungart. Journal of the Acoustical Society of America, 2001
972 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Speech and Hearing, Signal Processing
View Paper: https://www.semanticscholar.org/paper/ef70143fce4eefae0739677fd7d9528b686e09ce
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Yi Luo
- Nima Mesgarani
- Deliang Wang
- Nai Ding
- Jonathan Z. Simon
- Zhuo Chen
- Matthew H. Davis
- Sophie K. Scott
In Speech and Hearing
- Sophie K. Scott
- Nima Mesgarani
- Nai Ding
- Ann Bradlow
- Sven L. Mattys
- Matthew H. Davis
- Jonathan Z. Simon
- Deliang Wang
In Signal Processing
- Yi Luo
- Zhuo Chen
- Nima Mesgarani
- Shinji Watanabe
- Deliang Wang
- Jonathan Le Roux
- John R. Hershey
- Jinyu Li
Citations in the corpus
- Speech recognition in adverse conditions: A review (2012, background): “, Brungart, Simpson, Darwin, Arbogast, & Kidd, 2005) and the duration and nature of the glimpses of speech (e.g., Cooke, 2006). While energetic masking generally leads to greater influence of higherorder knowledge (e.g., Mayo et al., 1997), Mattys et al. (2009) found that lexical access can in some ”
- Divide and Conquer: A Deep CASA Approach to Talker-Independent Monaural Speaker Separation (2019, background): “Although human listeners excel at attending to a target speaker even without any spatial cues [4], speech separation remains a challenge for machines despite decades of research.”
- Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals (2020, background): “This direction is motivated by the so-called informational masking effect in the cocktail party problem [4, 17], where the linguistic clue may exert a strong influence on the outcome of speech-to-speech perception.”
- Emergence of neural encoding of auditory objects while listening to competing speakers (2012, background): “When the intensity of either of the two competing speaker changes, up to 10 dB, human listeners can still understand either speaker with more than 50% intelligibility (10).”
- Speaker-Independent Speech Separation With Deep Attractor Network (2017, background): “n about the speakers is available, a problem known as speaker-independent speech separation. Humans are particularly adept at this task, even in the absence of any spatial separation between speakers [3], [4], [5]. This effortless task for humans, however, has proven difficult to model and emulate al”