The native speaker of English - A clash of conceptualisations : A comparative analysis of self-ascribed and non-elective native/non-native English speaker identity

The native speaker is a contentious linguistic concept, and since there has yet to be a consensus on its definition, the perspectives surrounding the concept are diverse and sometimes conflicting. This is nowhere more visible than in the debate on the native English speaker (NES). Countries such as the USA, Canada, UK, Ireland, Australia and New Zealand are unquestionably considered home to NESs; however, the population of some Caribbean countries also speaks English as their first language, and furthermore, today s ESL (English as a second language) countries contain an increasing number of people for whom English is a dominant language in their everyday life. The unfamiliarity of most laypeople with the complexity of the NES concept can thus lead to misconceptions of some speakers NES identity, as well as to linguistic discrimination. The main aim of this study, therefore, has been to explore native and non-native English speaker (NNES) identity constructs from both a personal perspective (self-ascribed identity) and a societal one (non-elective identity). These were subsequently compared and contrasted in order to provide a comprehensive picture of the most significant linguistic and social factors for different NES conceptualisations. Since previous research on the concept predominantly focused only on one perspective or definition of the NES, this study has taken a more complex approach, by utilising two distinct datasets and methods. The method used to explore self-ascribed NES/NNES identities was a formal interview, which explored the interviewees social and linguistic background as well as their views on the native speaker concept. The non-elective NES constructs were analysed through a survey, which contained audio samples of the interviewees spoken English. These samples were played to Finnish university students, who were then asked to classify individual speakers as NES or NNES, to rate their accent, vocabulary, grammar, confidence and intelligibility, and to guess the speakers origin. The significance of the speech factors and the perceived country/area of origin in predicting NES classification was first explored through comparative data charts, after which it was statistically analysed by using a binary logistic regression model in SPSS. Results revealed discrepancies between self-ascribed and non-elective NES identity, and several instances proved particularly significant: Firstly, in the case of an EFL (English as a foreign language) speaker who had never lived in an English-speaking country, the fact that she possessed an American accent contributed greatly to her being largely considered a NES. Secondly, a speaker whose mother tongue and dominant language was English, but who was from the Caribbean and thus possessed a foreign-sounding accent, was third lowest in being classified as a NES. Thirdly, speakers from ESL countries were lowest in NES classification despite personally identifying as NES. Considering that the statistical analysis indicated speakers accent and perceived origin to be the most significant predictors of NES classification, it can be concluded that a native English speaker is still being conceptualised primarily as someone who comes from a dominant English-speaking country and thus possesses a relevant accent. Persons from any lesser-known English-speaking countries and ESL countries therefore sound foreign , become excluded from this concept, and may find their NES identity challenged

ACCDIST: A Metric for comparing speakers' accents

This paper introduces a new metric for the quantitative assessment of the similarity of speakers' accents. The ACCDIST metric is based on the correlation of inter-segment distance tables across speakers or groups. Basing the metric on segment similarity within a speaker ensures that it is sensitive to the speaker's pronunciation system rather than to his or her voice characteristics. The metric is shown to have an error rate of only 11% on the accent classification of speakers into 14 English regional accents of the British Isles, half the error rate of a metric based on spectral information directly. The metric may also be useful for cluster analysis of accent groups

The Names of Us English: Valley Girl, Cowboy, Yankee, Normal, Nasal, and Ignorant

A commonplace in United States (hereafter US) linguistics is that every region supports its own standard; none is the locus (or source) of the standard. Historically that is a fair assessment, for no long-term centre of culture, economy and government has dominated in the US

Assimilation of Voicing in Czech Speakers of English: The Effect of the Degree of Accentedness

Czech and English are languages which differ with respect to the implementation of voicing. Unlike in English, there is a considerable agreement between phonological (systemic) and phonetic (actual) voicing in Czech, and, more importantly, the two languages have different strategies for the assimilation of voicing across the word boundary. The present study investigates the voicing in word-final obstruents in Czech speakers of English with the specific aim of ascertaining whether the degree of the speakers’ foreign accent correlates with the way they treat English obstruents in assimilatory contexts. L2 speakers, divided into three groups of varying accentedness, were examined employing categorization and a voicing profile method for establishing the presence/absence of voicing. The results suggest that speakers with a different degree of Czech accent do differ in their realization of voicing in the way predicted by a negative transfer of assimilatory habits from Czech

Comprehension of familiar and unfamiliar native accents under adverse listening conditions

This study aimed to determine the relative processing cost associated with comprehension of an unfamiliar native accent under adverse listening conditions. Two sentence verification experiments were conducted in which listeners heard sentences at various signal-to-noise ratios. In Experiment 1, these sentences were spoken in a familiar or an unfamiliar native accent or in two familiar native accents. In Experiment 2, they were spoken in a familiar or unfamiliar native accent or in a nonnative accent. The results indicated that the differences between the native accents influenced the speed of language processing under adverse listening conditions and that this processing speed was modulated by the relative familiarity of the listener with the native accent. Furthermore, the results showed that the processing cost associated with the nonnative accent was larger than for the unfamiliar native accent

Prosodic Event Recognition using Convolutional Neural Networks with Context Information

This paper demonstrates the potential of convolutional neural networks (CNN) for detecting and classifying prosodic events on words, specifically pitch accents and phrase boundary tones, from frame-based acoustic features. Typical approaches use not only feature representations of the word in question but also its surrounding context. We show that adding position features indicating the current word benefits the CNN. In addition, this paper discusses the generalization from a speaker-dependent modelling approach to a speaker-independent setup. The proposed method is simple and efficient and yields strong results not only in speaker-dependent but also speaker-independent cases.Comment: Interspeech 2017 4 pages, 1 figur

The Role of Speaker Identification in Taiwanese Attitudes Towards Varieties of English

No abstract available