The Origins of New Zealand English project (ONZE) at the University of Canterbury houses a large audio corpus. Until a few years ago, this corpus was stored as a series of audio tapes and Microsoft Word documents. However, the corpus is now housed on a central server, and can be interacted with through the tailor-made software ‘ONZE Miner’. ONZE Miner is a digitally-interactive database that enables researchers to search across and interact with sound files. It houses time-aligned transcripts of the sound-files, which are tagged for phonological, grammatical and morphological information – all of which is searchable. The researcher can conduct acoustic analysis of sound files directly through the ONZE Miner interface. Search results can be exported into Excel, together with hypertext links to the relevant sound files. This paper describes the development and the architecture of the ONZE Miner software.
One piece of evidence adduced by George Kingsley Zipf for his eponymous law (Zipf, 1935) and its explanation of the principle of least effort (Zipf, 1949) is the hypothesis that a word's polysemy is proportional to the square root of its frequency (Levelt, 2013). Pawley (2006) following Zipf, also proposes that 'there is a strong general correlation between frequency and the extent of polysemy'. This paper replicates Zipf 's approach but with data drawn from different sources to those available to Zipf, namely, for word frequency, the Kilgarriff most frequent word list drawn from the BNC (Kilgarriff, 1995) and, as a measure of polysemy, the WordNet data for the polysemy of the words in Kilgarriff's list. It also takes note of the syntactic category of lexemes. More advanced statistical modelling is used. Zipf 's observations are confirmed with some provisos. Their utility is examined. Explanations for this relationship remain to be established.
Automatically time-aligning utterances at the segmental level is increasingly common practice in phonetic and sociophonetic work because of the obvious benefits it brings in allowing the efficient scaling up of the amount of speech data that can be analysed. The field is arriving at a set of recommended practices for improving alignment accuracy, but methodological differences across studies (e.g., the use of different languages and different measures of accuracy) often mean that direct comparison of the factors which facilitate or hinder alignment can be difficult. In this paper, following a review of the state of the art in automatic segmental alignment, we test the effects of a number of factors on its accuracy. Namely, we test the effects of: (1) the presence or absence of pause markers in the training data, (2) the presence of overlapping speech or other noise, (3) using training data from single or multiple speakers, (4) using different sampling rates, (5) using pre-trained acoustic models versus models trained ‘from scratch’, and (6) using different amounts of training data. For each test, we examine three different varieties of English, from New Zealand, the USA and the UK. The paper concludes with some recommendations for automatic segmental alignment in general.
In English, the predominance of stressed syllables as word onsets aids lexical segmentation in degraded listening conditions. Yet it is unlikely that these findings would readily transfer to languages with differing rhythmic structure. In the current study, the authors seek to examine whether listeners exploit both common word size (syllable number) and stress cues to aid lexical segmentation in Spanish. Forty-seven Spanish-speaking listeners transcribed two-word Spanish phrases in noise. As predicted by the statistical probabilities of Spanish, error analysis revealed that listeners preferred two- and three-syllable words with penultimate stress in their attempts to parse the degraded speech signal. These findings provide insight into the importance of stress in tandem with word size in the segmentation of Spanish words and suggest testable hypotheses for cross-linguistic studies that examine the effects of degraded acoustic cues on lexical segmentation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.