Publications

Abstract

While listening to meaningful speech, auditory input is processed more rapidly near the end (vs. beginning) of sentences. Although several studies have shown such word-to-word changes in auditory input processing, it is still unclear from which processing level these word-to-word dynamics originate. We investigated whether predictions derived from sentential context can result in auditory word-processing dynamics during sentence tracking. We presented healthy human participants with auditory stimuli consisting of word sequences, arranged into either predictable (coherent sentences) or less predictable (unstructured, random word sequences) 42-Hz amplitude-modulated speech, and a continuous 25-Hz amplitude-modulated distractor tone. We recorded RTs and frequency-tagged neuroelectric responses 1(auditory steady-state responses) to individual words at multiple temporal positions within the sentences, and quantified sentential context effects at each position while controlling for individual word characteristics (i.e., phonetics, frequency, and familiarity). We found that sentential context increasingly facilitates auditory word processing as evidenced by accelerated RTs and increased auditory steady-state responses to later-occurring words within sentences. These purely top–down contextually driven auditory word-processing dynamics occurred only when listeners focused their attention on the speech and did not transfer to the auditory processing of the concurrent distractor tone. These findings indicate that auditory word-processing dynamics during sentence tracking can originate from sentential predictions. The predictions depend on the listeners' attention to the speech, and affect only the processing of the parsed speech, not that of concurrently presented auditory streams.

Abstract

Speech is perceived relative to the speech rate in the context. It is unclear, however, what information listeners use to compute speech rate. The present study examines whether listeners use the number of
syllables per unit time (i.e., syllable rate) as a measure of speech rate, as indexed by subsequent vowel perception. We ran two rate-normalization experiments in which participants heard duration-matched word lists that contained either monosyllabic
vs. bisyllabic words (Experiment 1), or monosyllabic vs. trisyllabic pseudowords (Experiment 2). The participants’ task was to categorize an /ɑ-aː/ continuum that followed the word lists. The monosyllabic condition was perceived as slower (i.e., fewer /aː/ responses) than the bisyllabic and
trisyllabic condition. However, no difference was observed between bisyllabic and trisyllabic contexts. Therefore, while syllable rate is used in perceiving speech rate, other factors, such as fast speech processes, mean F0, and intensity, must also influence rate normalization.

Abstract

When recognizing spoken words, listeners are confronted by variability in the speech signal caused by talker differences. Previous research has focused on segmental talker variability; less is known about how suprasegmental variability is handled. Here we investigated the use of perceptual learning to deal with between-talker differences in lexical stress. Two groups of participants heard Dutch minimal stress pairs (e.g., VOORnaam vs. voorNAAM, “first name” vs. “respectable”) spoken by two male talkers. Group 1 heard Talker 1 use only F0 to signal stress (intensity and duration values were ambiguous), while Talker 2 used only intensity (F0 and duration were ambiguous). Group 2 heard the reverse talker-cue mappings. After training, participants were tested on words from both talkers containing conflicting stress cues (“mixed items”; e.g., one spoken by Talker 1 with F0 signaling initial stress and intensity signaling final stress). We found that listeners used previously learned information about which talker used which cue to interpret the mixed items. For example, the mixed item described above tended to be interpreted as having initial stress by Group 1 but as having final stress by Group 2. This demonstrates that listeners learn how individual talkers signal stress and use that knowledge in spoken-word recognition.

Abstract

Over the course of a conversation, interlocutors sound more and more like each other in a process called convergence. However, the automaticity and grain size of convergence are not well established. This study therefore examined whether female native Dutch speakers converge to large yet sub-phonemic shifts in the F2 of the vowel /e/. Participants first performed a short reading task to establish baseline F2s for the vowel /e/, then shadowed 120 target words (alongside 360 fillers) which contained one instance of a manipulated vowel /e/ where the F2 had been shifted down to that of the vowel /ø/. Consistent exposure to large (sub-phonemic) downward shifts in F2 did not result in convergence. The results raise issues for theories which view convergence as a product of automatic integration between perception and production.

Abstract

Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means
of transcription tasks (‘type out what you hear’). The intelligibility of a given stimulus is then often
expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring
the participants’ raw responses for words correctly identified from the target sentence is a time-
consuming task, and hence resource-intensive. Moreover, there is no consensus among speech
scientists about what specific protocol to use for the human scoring, limiting the reliability of
human scores. The present paper evaluates various forms of fuzzy string matching between
participants’ responses and target sentences, as automated metrics of listener transcript accuracy.
We demonstrate that one particular metric, the Token Sort Ratio, is a consistent, highly efficient,
and accurate metric for automated assessment of listener transcripts, as evidenced by high
correlations with human-generated scores (best correlation: r = 0.940) and a strong relationship to
acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for
assessment of listener transcript accuracy in large-scale speech intelligibility studies. See
https://tokensortratio.netlify.app for an online implementation.

Abstract

Speech in everyday conversations is riddled with discourse markers (DMs), such as well, you know, and like. However, in many lab-based studies of speech comprehension, such DMs are typically absent from the carefully articulated and highly controlled speech stimuli. As such, little is known about how these DMs influence online word recognition. The present study specifically investigated the online processing of DM like and how it influences the activation of words in the mental lexicon. We specifically targeted the cohort competitor (CC) effect in the Visual World Paradigm: Upon hearing spoken instructions to “pick up the beaker,” human listeners also typically fixate—next to the target object—referents that overlap phonologically with the target word (cohort competitors such as beetle; CCs). However, several studies have argued that CC effects are constrained by syntactic, semantic, pragmatic, and discourse constraints. Therefore, the present study investigated whether DM like influences online word recognition by activating its cohort competitors (e.g., lightbulb). In an eye-tracking experiment using the Visual World Paradigm, we demonstrate that when participants heard spoken instructions such as “Now press the button for the, like … unicycle,” they showed anticipatory looks to the CC referent (lightbulb)well before hearing the target. This CC effect was sustained for a relatively long period of time, even despite hearing disambiguating information (i.e., the /k/ in like). Analysis of the reaction times also showed that participants were significantly faster to select CC targets (lightbulb) when preceded by DM like. These findings suggest that seemingly trivial DMs, such as like, activate their CCs, impacting online word recognition. Thus, we advocate a more holistic perspective on spoken language comprehension in naturalistic communication, including the processing of DMs.

Abstract

Beat gestures—spontaneously produced biphasic movements of the hand—
are among the most frequently encountered co-speech gestures in human
communication. They are closely temporally aligned to the prosodic charac-
teristics of the speech signal, typically occurring on lexically stressed
syllables. Despite their prevalence across speakers of the world’s languages,
how beat gestures impact spoken word recognition is unclear. Can these
simple ‘flicks of the hand’ influence speech perception? Across a range
of experiments, we demonstrate that beat gestures influence the explicit
and implicit perception of lexical stress (e.g. distinguishing OBject from
obJECT), and in turn can influence what vowels listeners hear. Thus, we pro-
vide converging evidence for a manual McGurk effect: relatively simple and
widely occurring hand movements influence which speech sounds we hear

Abstract

Speech contains pronounced amplitude modulations in the 1–9 Hz range, correlating with the syllabic rate of speech. Recent models of speech perception propose that this rhythmic nature of speech is central to speech recognition and has beneficial effects on language processing. Here, we investigated the contribution of amplitude modulations to the subjective impression listeners have of public speakers. The speech from US presidential candidates Hillary Clinton and Donald Trump in the three TV debates of 2016 was acoustically analyzed by means of modulation spectra. These indicated that Clinton’s speech had more pronounced amplitude modulations than Trump’s speech, particularly in the 1–9 Hz range. A subsequent perception experiment, with listeners rating the perceived charisma of (low-pass filtered versions of) Clinton’s and Trump’s speech, showed that more pronounced amplitude modulations (i.e., more ‘rhythmic’ speech) increased perceived charisma ratings. These outcomes highlight the important contribution of speech rhythm to charisma perception.

Abstract

Despite advances in automatic speech recognition (ASR), human input is still essential to produce research-grade segmentations of speech data. Con- ventional approaches to manual segmentation are very labour-intensive. We introduce POnSS, a browser-based system that is specialized for the task of segmenting the onsets and offsets of words, that combines aspects of ASR with limited human input. In developing POnSS, we identified several sub- tasks of segmentation, and implemented each of these as separate interfaces for the annotators to interact with, to streamline their task as much as possible. We evaluated segmentations made with POnSS against a base- line of segmentations of the same data made conventionally in Praat. We observed that POnSS achieved comparable reliability to segmentation us- ing Praat, but required 23% less annotator time investment. Because of its greater efficiency without sacrificing reliability, POnSS represents a distinct methodological advance for the segmentation of speech data.

Abstract

One of the challenges in speech perception is that listeners must deal with considerable
segmental and suprasegmental variability in the acoustic signal due to differences between talkers. Most previous studies have focused on how listeners deal with segmental variability.
In this EEG experiment, we investigated whether listeners track talker-specific usage of suprasegmental cues to lexical stress to recognize spoken words correctly. In a three-day training phase, Dutch participants learned to map non-word minimal stress pairs onto different object referents (e.g., USklot meant “lamp”; usKLOT meant “train”). These non-words were
produced by two male talkers. Critically, each talker used only one suprasegmental cue to signal stress (e.g., Talker A used only F0 and Talker B only intensity). We expected participants to learn which talker used which cue to signal stress. In the test phase, participants indicated whether spoken sentences including these non-words were correct (“The word for lamp is…”).
We found that participants were slower to indicate that a stimulus was correct if the non-word was produced with the unexpected cue (e.g., Talker A using intensity). That is, if in training Talker A used F0 to signal stress, participants experienced a mismatch between predicted and perceived phonological word-forms if, at test, Talker A unexpectedly used intensity to cue
stress. In contrast, the N200 amplitude, an event-related potential related to phonological
prediction, was not modulated by the cue mismatch. Theoretical implications of these
contrasting results are discussed. The behavioral findings illustrate talker-specific prediction of prosodic cues, picked up through perceptual learning during training.

Abstract

Disfluencies, such as uh and uhm, are known to help the listener in speech comprehension. For instance, disfluencies may elicit prediction of less accessible referents and may trigger listeners’ attention to the following word. However, recent work suggests differential processing of disfluencies in native and non-native speech. The current study investigated whether the beneficial effects of disfluencies on listeners’ attention are modulated by the (non-)native identity of the speaker. Using the Change Detection Paradigm, we investigated listeners’ recall accuracy for words presented in disfluent and fluent contexts, in native and non-native speech. We observed beneficial effects of both native and non-native disfluencies on listeners’ recall accuracy, suggesting that native and non-native disfluencies trigger listeners’ attention in a similar fashion.

Abstract

Speech perception involves a number of processes that deal with variation in the speech signal. One such process is normalization for speechrate: local temporal cues are perceived relative to the rate in the surrounding context. It is as yet unclear whether and how this perceptual effect interacts with higher level impressions of rate, such as a speaker’s nonnative identity. Nonnative speakers typically speak more slowly than natives, an experience that listeners take into account when explicitly judging the rate of nonnative speech. The present study investigated whether this is also reflected in implicit rate normalization. Results indicate that nonnative speech is implicitly perceived as faster than temporally-matched native speech, suggesting that the additional cognitive load of listening to an accent speeds up rate perception. Therefore, rate perception in speech is not dependent on syllable durations alone but also on the ease of processing of the temporal signal.