Publications

Abstract

Accommodating talker variability is a complex and multi-layered cognitive process. It involves shifting attention to the vocal characteristics of the talker as well as the linguistic content of their speech. Due to an interdependence between voice and phonological processing, multi-talker
environments typically incur additional processing costs compared to single-talker environments. A failure or inability to efficiently distribute attention over multiple acoustic cues in the speech signal
may have detrimental language learning consequences. Yet, no studies have examined effects of multi-talker processing in populations with atypical perceptual, social and language processing for communication, including autistic people. Employing a classic word-monitoring task, we investigated
effects of talker variability in Australian English autistic (n = 24) and non-autistic (n = 28) adults.
Listeners responded to target words (e.g., apple, duck, corn) in randomised sequences of words. Half of the sequences were spoken by a single talker and the other half by multiple talkers. Results revealed that autistic participants’ sensitivity scores to accurately-spotted target words did not differ to those
of non-autistic participants, regardless of whether they were spoken by a single or multiple talkers. As expected, the non-autistic group showed the well-established processing cost associated with talker
variability (e.g., slower response times). Remarkably, autistic listeners’ response times did not differ across single- or multi-talker conditions, indicating they did not show perceptual processing costs
when accommodating talker variability. The present findings have implications for theories of autistic perception and speech and language processing.

Abstract

Not only can the pitfalls that Firestone & Scholl (F&S) identify be generalised across multiple studies within the field of visual perception, but also they have general application outside the field wherever perceptual and cognitive processing are compared. We call attention to the widespread susceptibility of research on the perception of speech to versions of the same pitfalls.

Abstract

To examine the relative roles of language-specific and language-universal mechanisms in the production of prosodic focus, we compared production of five different types of focus by native speakers of English and Mandarin. Two comparable dialogues were constructed for each language, with the same words appearing in focused and unfocused position; 24 speakers recorded each dialogue in each language. Duration, F0 (mean, maximum, range), and rms-intensity (mean, maximum) of all critical word tokens were measured. Across the different types of focus, cross-language differences were observed in the degree to which English versus Mandarin speakers use the different prosodic parameters to mark focus, suggesting that while prosody may be universally available for expressing focus, the means of its employment may be considerably language-specific

Abstract

In Korean, focus is expressed in accentual phrasing. To ascertain whether words focused in this manner enjoy a processing advantage analogous to that conferred by focus as expressed in, e.g, English and Dutch, we devised sentences with target words in one of four conditions: prosodic focus, syntactic focus, prosodic + syntactic focus, and no focus as a control. 32 native speakers of Korean listened to blocks of 10 sentences, then were presented visually with words and asked whether or not they had heard them. Overall, words with focus were recognised significantly faster and more accurately than unfocused words. In addition, words with syntactic focus or syntactic + prosodic focus were recognised faster than words with prosodic focus alone. As for other languages, Korean focus confers processing advantage on the words carrying it. While prosodic focus does provide an advantage, however, syntactic focus appears to provide the greater beneficial effect for recognition memory

Abstract

Speech perception involves prediction, but how is that prediction implemented? In cognitive models prediction has often been taken to imply that there is feedback of activation from lexical to pre-lexical processes as implemented in interactive-activation models (IAMs). We show that simple activation feedback does not actually improve speech recognition. However, other forms of feedback can be beneficial. In particular, feedback can enable the listener to adapt to changing input, and can potentially help the listener to recognise unusual input, or recognise speech in the presence of competing sounds. The common feature of these helpful forms of feedback is that they are all ways of optimising the performance of speech recognition using Bayesian inference. That is, listeners make predictions about speech because speech recognition is optimal in the sense captured in Bayesian models.

Abstract

If a conversation is to proceed smoothly, the participants have to take turns to speak. Studies of conversation have shown that there are signals which speakers give to inform listeners that they are willing to hand over the conversational turn1−4. Some of these signals are part of the text (for example, completion of syntactic segments), some are non-verbal (such as completion of a gesture), but most are carried by the pitch, timing and intensity pattern of the speech; for example, both pitch and loudness tend to drop particularly low at the end of a speaker's turn. When one speaker interrupts another, the two can be said to be disputing who has the turn. Interruptions can occur because one participant tries to dominate or disrupt the conversation. But it could also be the case that mistakes occur in the way these subtle turn-yielding signals are transmitted and received. We demonstrate here that many interruptions in an interview with Mrs Margaret Thatcher, the British Prime Minister, occur at points where independent judges agree that her turn appears to have finished. It is suggested that she is unconsciously displaying turn-yielding cues at certain inappropriate points. The turn-yielding cues responsible are identified.