Publications

Abstract

In natural communication, we typically both see and hear our conversation partner. Speech comprehension thus requires the integration of auditory and visual information from the speech signal. This is for instance evidenced by the Manual McGurk effect, where the perception of lexical stress is biased towards the syllable that has a beat gesture aligned to it. However, there is considerable individual variation in how heavily gestural timing is weighed as a cue to stress. To assess within-individualconsistency, this study investigated the test-retest reliability of the Manual McGurk effect. We reran an earlier Manual McGurk experiment with the same participants, over 1.5 years later. At the group level, we successfully replicated the Manual McGurk effect with a similar effect size. However, a correlation of the by-participant effect sizes in the two identical experiments indicated that there was only a weak correlation between both tests, suggesting that the weighing of gestural information in the perception of lexical stress is stable at the group level, but less so in individuals. Findings are discussed in comparison to other measures of audiovisual integration in speech perception. Index Terms: Audiovisual integration, beat gestures, lexical stress, test-retest reliability

Abstract

The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are `open weight' at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.

Abstract

The human brain deals with the infinite variability of speech through multiple mechanisms. Some of them rely solely on information in the speech input (i.e., signal-driven) whereas some rely on linguistic or real-world knowledge (i.e., knowledge-driven). Many signal-driven perceptual processes rely on the enhancement of acoustic differences between incoming speech sounds, producing contrastive adjustments. For instance, when an ambiguous voiceless fricative is preceded by a high fundamental frequency (f0) sentence, the fricative is perceived as having lower a spectral center of gravity (CoG). However, it is not clear whether knowledge of a talker’s typical f0 can lead to similar contrastive effects. This study investigated a possible talker f0 effect on fricative CoG perception. In the exposure phase, two groups of participants (N=16 each) heard the same talker at high or low f0 for 20 minutes. Later, in the test phase, participants rated fixed-f0 /?ɔk/ tokens as being /sɔk/ (i.e., high CoG) or /ʃɔk/ (i.e., low CoG), where /?/ represents a fricative from a 5-step /s/-/ʃ/ continuum. Surprisingly, the data revealed the opposite of our contrastive hypothesis, whereby hearing high f0 instead biased perception towards high CoG. Thus, we demonstrated that talker f0 information affects fricative CoG perception.