ABSTRACT
Previous research estimating vowel formant discrimination thresholds in words and sentences has often employed a modified two-alternative-forced-choice (2AFC) task with adaptive tracking. Although this approach has produced stable data, the length and number of experimental sessions, as well as the unnaturalness of the task, limit generalizations of results to ordinary speech communication. In this exploratory study, a typical identification task was used to estimate vowel formant discrimination thresholds. Specifically, a signal detection theory approach was used to develop a method to estimate vowel formant discrimination thresholds from a quicker, more natural single-interval classification task. In experiment 1 "classification thresholds" for words in isolation and embedded in sentences were compared to previously collected 2AFC data. Experiment 2 used a within-subjects design to compare thresholds estimated from both classification and 2AFC tasks. Due to instabilities observed in the experiment 1 sentence data, experiment 2 examined only isolated words. Results from these experiments show that for isolated words, thresholds estimated using the classification procedure are comparable to those estimated using the 2AFC task. These results, as well as an analysis of several aspects of the classification procedure, support the viability of this new approach for estimating discrimination thresholds for speech stimuli.
Subject(s)
Phonetics , Signal Detection, Psychological , Speech Acoustics , Speech Perception , Differential Threshold , Humans , Logistic Models , Semantics , SpeechABSTRACT
Examining phonetic categorization in multidimensional stimulus spaces poses a number of practical problems. The traditional method of forced identification becomes prohibitive when the number and size of stimulus dimensions becomes increasingly large. In response, Evans and Iverson [J. Acoust. Soc. Am. 115, 352-361 (2004)] proposed an adaptive tracking algorithm for finding vowel best exemplars in a multidimensional space. This algorithm converged on best exemplars in a small number of trials; however, the search method was designed explicitly for vowel stimuli. In this paper, a more general multidimensional search algorithm is described, and results from simulations and experiments using the proposed algorithm are presented.
