Human and computer recognition of regional accents and ethnic groups from British English speech

Abstract

Language Recognition Evaluation (LRE) tasks. This 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 fourteen regional accents of British English in the ABI-1 corpus (good quality read speech), our language ID system achieves a recognition accuracy of 86.4%, 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 language ID system distinguishes between these two groups with an accuracy of 94.3% compared with 90.24% for human listeners. Although direct comparison is difficult, it seems that our language ID system performs much better on the standard twelve class NIST 2003 Language Recognition Evaluation task or the two class ethnic group recognition task than on the fourteen class regional accent recognition task. We conclude that automatic accent recognition is a challenging task for speech technology, and that the use of natural conversational speech may be advantageous for these types of paralinguistic task.One issue with conventional approaches to language ID that use high-order Gaussian Mixture Models (GMMs) and high-dimensional feature vectors is the amount of computing power that they require. Currently, multi-core Graphics Processing Units (GPUs) provide a possible solution at very little cost. In this thesis we also explore the application of GPUs to speech signal and pattern processing, using language ID as a vehicle to demonstrate their benefits. Realisation of the full potential of GPUs requires both effective coding of predetermined algorithms, and, in cases where there is a choice, selection of the algorithm or technique for a specific function that is most able to exploit the properties of the GPU. We demonstrate these principles using the NIST LRE 2003 task, which involves processing over 600 hours of speech. We focus on two parts of the system, namely the acoustic classifier, which is based on a 2048 component GMM, and the acoustic feature extraction process. In the case of the latter we compare a conventional FFT-based analysis with an FIR filter bank, both in terms of their ability to exploit the GPU architecture and language ID performance. With no increase in error rate our GPU based system, with an FIR-based front-end, completes the full NIST LRE 2003 task in 16 hours, compared with 180 hours for the more conventional FFT-based system on a standard CPU (a speed up factor of more than 11).

So Sánh Một Số Bộ Phân Lớp Dùng Cho Nhận Dạng Phương Ngữ Tiếng Việt

“…Vì vậy, việc khai thác thông tin F0 dùng làm đặc trưng cho hệ thống tự động nhận dạng phương ngữ cũng là yếu tố cần thiết. Trong số các mô hình được sử dụng cho các hệ thống xử lý tiếng nói như định danh ngôn ngữ, định danh người nói… SVM đã được áp dụng khá phổ biến và cho phép các hệ thống này đạt hiệu năng cao [4], [5], [6], [7], [8], [9]. SVM có thể được sử dụng độc lập hoặc kết hợp với GMM để nhận dạng phương ngữ [10], [11], [12], [13], [14], [15], [16].…”

Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition

“…Recent language/dialect recognition techniques can be divided into phonotactic, and acoustic approaches [7]. Since phonotactic features and acoustic (spectral and/or prosodic) features provide complementary cues, state-of-the-art methods usually apply a combination of both through a fusion of their output scores [7].…”

“…Since phonotactic features and acoustic (spectral and/or prosodic) features provide complementary cues, state-of-the-art methods usually apply a combination of both through a fusion of their output scores [7]. A phone recognizer followed by language models (PRLM), parallel PRLM (PPRLM) and support vector machines PRLM techniques developed within the language recognition area, are successful phonotactic methods focusing on phone sequences as an important characteristic of different accents [8], [9].…”

“…L ANGUAGE and dialect/accent recognition has received increased attention during the recent decades due to its importance for the enhancement of automatic speech recognition (ASR) [1], [2], multi-language translation systems, service customization, targeted advertising, and forensics softwares [3], [4].…”

Automatic pronunciation clustering using a World English archive and pronunciation structure analysis

“…One is collecting data and labeling them, and the other is creating a good algorithm of drawing the global map for a huge amount of unlabeled data. In [6], some accented English corpora with good quality were introduced. However, labeling data is needed in this paper.…”