A glimpsing model of speech perception in noise.
By Martin P. Cooke. Journal of the Acoustical Society of America, 2006
766 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Signal Processing
View Paper: https://www.semanticscholar.org/paper/2de92fdac5acd93a0ece7796142d52cd9afee8a2
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
- Jesper Højvang Jensen
- Cees H. Taal
- Bowen Shi
- Wei-Ning Hsu
- Jacob Donley
- Matthew H. Davis
- John H. L. Hansen
- Nitish Krishnamurthy
In Signal Processing
- Bowen Shi
- Wei-Ning Hsu
- Yossi Adi
- Jesper Højvang Jensen
- Cees H. Taal
- Tal Remez
- Jacob Donley
- John H. L. Hansen
Citations in the corpus
- Non-native speech perception in adverse conditions: A review (2010, background): “• Intelligible maskers in an L2 appear to reduce L1 interference and hence aid non-native listeners (Rhebergen et al., 2005; García Lecumberri and Cooke, 2006; Van Engen and Bradlow, 2007).”
- ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement (2022, background): “3 presents results of audio-visual speech denoising on four test splits: lvl 1-4, which contain noisy samples of SNR in [10, 20], [0, 10], [-10, 0], and [-20, -10].”
- Babble Noise: Modeling, Analysis, and Applications (2009, background): “This noise is uniquely challenging because of its highly time evolving structure and its similarity to the desired target speech.”
- Speech recognition in adverse conditions: A review (2012, background): “, Cooke et al., 2008; Garcia Lecumberri & Cooke, 2006). In this case, the attentional effort can be described as the cost of trying to ignore the distractor and selectively attending to the target. In contrast, ACs incurred under multi-tasking conditions (e.g., speech recognition while driving or mo”
- An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers (2016, methodology): “…methods may be divided into two classes: 1) methods which require that the target speech signal and the distorting component (e.g., the additive noise) are available in separation, e.g., [3], [4], [6], [8], [13] and 2) methods which do not impose this requirement, e.g., [9], [10], [14], [17].”