Publications

Abstract

We present written naming norms from 153 young adult Dutch speakers for 1397 photographs (the BOSS set; see Brodeur, Dionne-Dostie, Montreuil, & Lepage, 2010; Brodeur, Guérard, & Bouras, 2014). From the norming study, we report the preferred (modal) name, alternative names, name agreement, and average object agreement. In addition, the data base includes Zipf frequency, word prevalence and Age of Acquisition for the modal picture names collected. Furthermore, we describe a subset of 359 photographs with very good name agreement and a subset of 35 photos with two common names. These sets may be particularly valuable for designing experiments. Though the participants typed the object names, comparisons with other datasets indicate that the collected norms are valuable for spoken naming studies as well.

Abstract

Natural conversations are characterized by short transition times between turns. This holds in particular for multi-party conversations. The short turn transitions in everyday conversations contrast sharply with the much longer speech onset latencies observed in laboratory studies where speakers respond to spoken utterances. There are many factors that facilitate speech production in conversational compared to laboratory settings. Here we highlight one of them, the impact of competition for turns. In multi-party conversations, speakers often compete for turns. In quantitative corpus analyses of multi-party conversation, the fastest response determines the recorded turn transition time. In contrast, in dyadic conversations such competition for turns is much less likely to arise, and in laboratory experiments with individual participants it does not arise at all. Therefore, all responses tend to be recorded. Thus, competition for turns may reduce the recorded mean turn transition times in multi-party conversations for a simple statistical reason: slow responses are not included in the means. We report two studies illustrating this point. We first report the results of simulations showing how much the response times in a laboratory experiment would be reduced if, for each trial, instead of recording all responses, only the fastest responses of several participants responding independently on the trial were recorded. We then present results from a quantitative corpus analysis comparing turn transition times in dyadic and triadic conversations. There was no significant group size effect in question-response transition times, where the present speaker often selects the next one, thus reducing competition between speakers. But, as predicted, triads showed shorter turn transition times than dyads for the remaining turn transitions, where competition for the floor was more likely to arise. Together, these data show that turn transition times in conversation should be interpreted in the context of group size, turn transition type, and social setting.

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.