Factors influencing automatic segmental alignment of sociophonetic corpora

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.