Publications

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

Prosodic juncture concerns the compartmentalization and partitioning of syntactic entities in spoken discourse by means of prosody. It has been argued that the Intonation Unit, defined by internal criteria and prosodic boundary phenomena (e.g., final lengthening, pitch reset, pauses), encapsulates the basic structural unit of spoken Modern Hebrew.

Abstract

Fricative consonants in Hebrew can be divided into bgdkpt and sibilants (ז, ס, צ, שׁ, שׂ). Hebrew sibilants have been argued to stem from Proto-Semitic affricates, laterals, interdentals and /s/. In standard Israeli Hebrew the sibilants are pronounced as [s] (ס and שׂ), [ʃ] (שׁ), [z] (ז), [ʦ] (צ).

Abstract

The oral fluency level of an L2 speaker is often used as a measure in assessing language proficiency. The present study reports on four experiments investigating the contributions of three fluency aspects (pauses, speed and repairs) to perceived fluency. In Experiment 1 untrained raters evaluated the oral fluency of L2 Dutch speakers. Using specific acoustic measures of pause, speed and repair phenomena, linear regression analyses revealed that pause and speed measures best predicted the subjective fluency ratings, and that repair measures contributed only very little. A second research question sought to account for these results by investigating perceptual sensitivity to acoustic pause, speed and repair phenomena, possibly accounting for the results from Experiment 1. In Experiments 2–4 three new groups of untrained raters rated the same L2 speech materials from Experiment 1 on the use of pauses, speed and repairs. A comparison of the results from perceptual sensitivity (Experiments 2–4) with fluency perception (Experiment 1) showed that perceptual sensitivity alone could not account for the contributions of the three aspects to perceived fluency. We conclude that listeners weigh the importance of the perceived aspects of fluency to come to an overall judgment.

Abstract

Second language (L2) research often involves analyses of acoustic measures of fluency. The studies investigating fluency, however, have been difficult to compare because the measures of fluency that were used differed widely. One of the differences between studies concerns the lower cut-off point for silent pauses, which has been set anywhere between 100 ms and 1000 ms. The goal of this paper is to find an optimal cut-off point. We calculate acoustic measures of fluency using different pause thresholds and then relate these measures to a measure of L2 proficiency and to ratings on fluency.