Shortlist B: a Bayesian model of continuous speech recognition.

By Dennis Norris, James M. McQueen. Psychology Review, 2008

658 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/85a0098d7871c88e6e60509c208b854b267c1f86

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

Non-native speech perception in adverse conditions: A review (2010, background): “The most recent model (Norris and McQueen, 2008) allows various sources of information to be evaluated probabilistically, and this is also the first model which has fully incorporated frequency information, known to play a significant role in speech processing.”
What do we mean by prediction in language comprehension? (2015, background): “…level can continue to influence the activation of the phonetic/phonological input that is currently being processed, such as lexical activity to fish leading to further enhancement of activity to /fl/ (see Norris, 1994; Norris & McQueen, 2008; Norris, McQueen, & Cutler, 2000 for discussion).”
Speech recognition in adverse conditions: A review (2012, background): “For example, in a Bayesian view of speech recognition (e.g., Norris & McQueen, 2008), the influence of prior probability, e.g., lexical-semantic knowledge, is greatest when there are multiple plausible interpretations of the speech signal*e.g., due to noise or ambiguity in the signal.”
Demythologizing the word frequency effect: A discriminative learning perspective (2010, methodology): “In the Bayesian Reader of Norris (2006) as well as in the Shortlist-B model of Norris and McQueen (2008), word frequency comes into play, in the calculation of the posterior probability of a word given the visual or auditory input, as the estimate of that word’s long-term a-priori probability.”
Frequency in lexical processing (2016, background): “…activation levels or activation thresholds in interactive activation and spreading activation models, and also the priors of Bayesian models (e.g., see Norris, 2006; Norris & McQueen, 2008), are required by theories failing to reflect on the learning involved for discriminating between words.”
The roles of tonal and segmental information in Mandarin spoken word recognition: An eyetracking study (2010, background): “This conceptualization is the inspiration for a number of current models of spoken word recognition (such as TRACE: McClelland & Elman, 1986 and Shortlist/ Merge: Norris, 1994; Norris & McQueen, 2008; Norris, McQueen, & Cutler, 2000).”