Computational Modeling of an Auditory Lexical Decision Experiment Using DIANA.

We present an implementation of DIANA, a computational model of spoken word recognition, to model responses collected in the Massive Auditory Lexical Decision (MALD) project. DIANA is an end-to-end model, including an activation and decision component that takes the acoustic signal as input, activates internal word representations, and outputs lexicality judgments and estimated response latencies. Simulation 1 presents the process of creating acoustic models required by DIANA to analyze novel speech input. Simulation 2 investigates DIANA's performance in determining whether the input signal is a word present in the lexicon or a pseudoword. In Simulation 3, we generate estimates of response latency and correlate them with general tendencies in participant responses in MALD data. We find that DIANA performs fairly well in free word recognition and lexical decision. However, the current approach for estimating response latency provides estimates opposite to those found in behavioral data. We discuss these findings and offer suggestions as to what a contemporary model of spoken word recognition should be able to do.

Semantic richness effects in isolated spoken word recognition: Evidence from massive auditory lexical decision.

While known to influence visual lexical processing, the semantic information we associate with words has recently been found to influence auditory lexical processing as well. The present work explored the influence of semantic richness in auditory lexical decision. Study 1 recreated an experiment investigating semantic richness effects in concrete nouns (Goh et al., 2016). In Study 2, we expanded the stimulus set from 442 to 8,626 items, exploring the robustness of effects observed in Study 1 against a larger data set with increased diversity in both word class and other characteristics of interest. We also utilized generalized additive mixed models to investigate potential nonlinear effects. Results indicate that semantic richness effects become more nuanced and detectable when a wider set of items belonging to different parts of speech is examined. Findings are discussed in the context of models of spoken word recognition. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Perception of vowels with missing formant peaks.

Although the first two or three formant frequencies are considered essential cues for vowel identification, certain limitations of this approach have been noted. Alternative explanations have suggested listeners rely on other aspects of the gross spectral shape. A study conducted by Ito, Tsuchida, and Yano [(2001). J. Acoust. Soc. Am. 110, 1141-1149] offered strong support for the latter, as attenuation of individual formant peaks left vowel identification largely unaffected. In the present study, these experiments are replicated in two dialects of English. Although the results were similar to those of Ito, Tsuchida, and Yano [(2001). J. Acoust. Soc. Am. 110, 1141-1149], quantitative analyses showed that when a formant is suppressed, participant response entropy increases due to increased listener uncertainty. In a subsequent experiment, using synthesized vowels with changing formant frequencies, suppressing individual formant peaks led to reliable changes in identification of certain vowels but not in others. These findings indicate that listeners can identify vowels with missing formant peaks. However, such formant-peak suppression may lead to decreased certainty in identification of steady-state vowels or even changes in vowel identification in certain dynamically specified vowels.

The Massive Auditory Lexical Decision (MALD) database.

The Massive Auditory Lexical Decision (MALD) database is an end-to-end, freely available auditory and production data set for speech and psycholinguistic research, providing time-aligned stimulus recordings for 26,793 words and 9592 pseudowords, and response data for 227,179 auditory lexical decisions from 231 unique monolingual English listeners. In addition to the experimental data, we provide many precompiled listener- and item-level descriptor variables. This data set makes it easy to explore responses, build and test theories, and compare a wide range of models. We present summary statistics and analyses.