The categorisation of speech sounds by adults and children: a study of the categorical perception hypothesis and the development weighting of acoustic speech cues

This thesis investigates the way adults and children perceive speech. With adult listeners, the question was whether speech is perceived categorically (categorical speech perception). With children, the question was whether there are age-related differences between the weights assigned to acoustic cues that specify certain speech contrasts (cue-weighting). One goal of this thesis was to test the categorical perception hypothesis. According to this hypothesis, the acquisition of the native phonological system changes listeners perception in such a way that they find it difficult to detect small differences between different realisations of the same phoneme, but relatively easy to detect equally small differences between realisations of two different phonemes. Several discrimination and classification experiments are described involving stimulus continua between vowels and between stop consonants. It is shown that categorical perception results are far from robust and that the degree of categorical perception is influenced by the discrimination task, the interstimulus interval, the listener, and the stimulus. Evidence is provided that listeners are perfectly capable of hearing small within-phoneme-category differences and thus that the acquisition of the phonological system does not have negative effects on the detection of differences between speech signals. A second goal was to study the development of the perceptual integration of speech cues that specify a certain phoneme category. It was tested whether children of 4, 6, and 9 years old weight certain acoustic cues for stop consonants, fricatives, and vowels differently from adults. The results confirm earlier findings that children, especially the 4-year-olds, weigh certain speech cues more heavily, and other cues less heavily, than adults do. Children s ability to adjust the weighting of specific cues in the variable acoustic signal provides further evidence against the idea that acoustic detail in the signal becomes less detectable as language develops. This study is of interest to phoneticians and psycholinguists, as well as to researchers working in the field of experimental speech and language development

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Mapping Acoustic and Semantic Dimensions of Auditory Perception

Auditory categorisation is a function of sensory perception which allows humans to generalise across many different sounds present in the environment and classify them into behaviourally relevant categories. These categories cover not only the variance of acoustic properties of the signal but also a wide variety of sound sources. However, it is unclear to what extent the acoustic structure of sound is associated with, and conveys, different facets of semantic category information. Whether people use such data and what drives their decisions when both acoustic and semantic information about the sound is available, also remains unknown. To answer these questions, we used the existing methods broadly practised in linguistics, acoustics and cognitive science, and bridged these domains by delineating their shared space. Firstly, we took a model-free exploratory approach to examine the underlying structure and inherent patterns in our dataset. To this end, we ran principal components, clustering and multidimensional scaling analyses. At the same time, we drew sound labels’ semantic space topography based on corpus-based word embeddings vectors. We then built an LDA model predicting class membership and compared the model-free approach and model predictions with the actual taxonomy. Finally, by conducting a series of web-based behavioural experiments, we investigated whether acoustic and semantic topographies relate to perceptual judgements. This analysis pipeline showed that natural sound categories could be successfully predicted based on the acoustic information alone and that perception of natural sound categories has some acoustic grounding. Results from our studies help to recognise the role of physical sound characteristics and their meaning in the process of sound perception and give an invaluable insight into the mechanisms governing the machine-based and human classifications