کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
563103 875471 2013 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Human and computer recognition of regional accents and ethnic groups from British English speech
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Human and computer recognition of regional accents and ethnic groups from British English speech
چکیده انگلیسی

The paralinguistic information in a speech signal includes clues to the geographical and social background of the speaker. This paper is concerned with automatic extraction of this information from a short segment of speech. A state-of-the-art language identification (LID) system is applied to the problems of regional accent recognition for British English, and ethnic group recognition within a particular accent. We compare the results with human performance and, for accent recognition, the ‘text dependent’ ACCDIST accent recognition measure. For the 14 regional accents of British English in the ABI-1 corpus (good quality read speech), our LID system achieves a recognition accuracy of 89.6%, compared with 95.18% for our best ACCDIST-based system and 58.24% for human listeners. The “Voices across Birmingham” corpus contains significant amounts of telephone conversational speech for the two largest ethnic groups in the city of Birmingham (UK), namely the ‘Asian’ and ‘White’ communities. Our LID system distinguishes between these two groups with an accuracy of 96.51% compared with 90.24% for human listeners. Although direct comparison is difficult, it seems that our LID system performs much better on the standard 12 class NIST 2003 Language Recognition Evaluation task or the two class ethnic group recognition task than on the 14 class regional accent recognition task. We conclude that automatic accent recognition is a challenging task for speech technology, and speculate that the use of natural conversational speech may be advantageous for these types of paralinguistic task.


► State-of-the-art language identification applied to accent and ethnicity recognition.
► Analysis of performance of acoustic and phonotactic components.
► Comparison with human classification and alternative automatic method (ACCDIST).
► Automatic methods outperform humans in both tasks.
► Difficulty of accent recognition confirmed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Speech & Language - Volume 27, Issue 1, January 2013, Pages 59–74
نویسندگان
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