Variability is perhaps the most notable characteristic of speech, and it is particularly noticeable in spontaneous conversational speech. The current research examines how speakers realize the American English stops /p, k, b, g/ and flaps (ɾ from /t, d/), in casual conversation and in careful speech. Target consonants appear after stressed syllables (e.g., "lobby") or between unstressed syllables (e.g., "humanity"), in one of six segmental/word-boundary environments. This work documents the degree and types of variability listeners encounter and must parse. Findings show greater reduction in connected and spontaneous speech, greater reduction in high frequency phrases (but not within high frequency words), and greater reduction between unstressed syllables than after a stress. Although highly reduced productions of stops and flaps occur often, with approximant-like tokens even in careful speech, reduction does not lead to a large amount of overlap between phonological categories. Approximant-like realizations of expected stops and flaps in some conditions constitute the majority of tokens. This shows that reduced speech is something that listeners encounter, and must perceive, in a large proportion of the speech they hear.

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.

AbstractMany studies report shorter acoustic durations, more coarticulation and reduced articulatory targets for frequent words. This study investigates a factor ignored in discussions on the relation between frequency and phonetic detail, namely, that motor skills improve with experience. Since frequency is a measure of experience, it follows that frequent words should show increased articulatory proficiency. We used EMA to test this prediction on German inflected verbs with [a] as stem vowels. Modeling median vertical tongue positions with quantile regression, we observed significant modulation by frequency of the U-shaped trajectory characterizing the articulation of the [a:]. These modulations reflect two constraints, one favoring smooth trajectories through anticipatory coarticulation, and one favoring clear articulation by realizing lower minima. The predominant pattern across sensors, exponents, and speech rate suggests that the constraint of clarity dominates for lower-frequency words. For medium-frequency words, the smoothness constraint leads to a raising of the trajectory. For the higher-frequency words, both constraints are met simultaneously, resulting in low minima and stronger coarticulation. These consequences of motor practice for articulation challenge both the common view that a higher-frequency of use comes with more articulatory reduction, and cognitive models of speech production positing that articulation is post-lexical.

The present study investigates the processing and production of four-word sequences such as I don't really know, at the age of, and I think it's the. Specifically, we investigate the influence of families of probabilistic measures such as unigram, bigram, trigram, and quadgram frequency of occurrence, logarithmic (log) probability of occurrence, and mutual information. Log probability of occurrence emerged as the predominant predictor family in the onset latency analysis, suggesting that recognition is mainly underpinned by competition between a target N-gram and its family members. In contrast, the amount of experience one has with an N-gram (frequency of occurrence) surfaced as the most prominent predictor in production. Further, probabilistic measures tied to trigrams surfaced as the best predictors in the onset latency analysis, while the measures tied to unigrams were most predictive of production durations.Finally, the interactions between probabilistic measures tied to unigrams, bigrams, trigrams, and quadgrams suggest that N-grams of different lengths are processed in parallel in both recognition and production.

The present paper investigates the influence of opposing lexical forces on speech production using the duration of the stem vowel of regular and irregular verbs as attested in the Buckeye corpus of conversational North-American English. We compared two sets of predictors, reflecting two different approaches to speechproduction, one based on competition between word forms, the other based on principles of discrimination learning. Classical measures in word form competition theories such as word frequency, lexical density, and gang size (types of vocalic alternation) were predictive of stem vowel duration. However, more precise predic-tions were obtained using measures derived from a two-layer network model trained on the Buckeye corpus. Measures representing strong bottom-up support predicted longer vowel durations. Conversely, measures reflecting uncertainty predicted shorter vowel durations, including a measure of the verb’s semantic density. The learning-based model also suggests that it is not a verb’s frequency as such that gives rise to shorter vowel duration, but rather a verb’s collocational diversity. Results are discussed with reference to the Smooth Signal Redundancy Hypothesis and the Paradigmatic Signal Enhancement Hypothesis.