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The para-linguistic information in a speech signal includes clues to the geographical and social background of the speaker. This paper is concerned with recognition of the 14 regional accents of British English. For Accent Identification (AID), acoustic methods exploit differences between the distributions of sounds, while phonotactic approaches exploit the sequences in which these sounds occur. We demonstrate these methods are good complements for each other and use their confusion matrices for further analysis. Our relatively simple i-vector and phonotactic fused system with recognition accuracy of 84.87% outperforms the i-vector fused results reported in literature, by 4.7%. Further analysis on distribution of British English accents has been carried out by analyzing the low dimensional representation of i-vector AID feature space. Index terms: Accent identification, I-vector, Phonotactic, British English regional accents 'short passages' (SPA, SPB and SPC), the 'short sentences' and the 'short phrases'. These are described below: • SPA, SPB and SPC are short paragraphs, of lengths 92, 92 and 107 words, respectively, which together form the accent-diagnostic 'sailor passage' (When a sailor in a

This paper presents an experimental study investigating the effect of frequency sub-bands on regional accent identification (AID) and speaker identification (SID) performance on the ABI-1 corpus. The AID and SID systems are based on Gaussian mixture modeling. The SID experiments show up to 100% accuracy when using the full 11.025 kHz bandwidth. The best AID performance of 60.34% is obtained when using band-pass filtered (0.23-3.4 kHz) speech.The experiments using isolated narrow sub-bands show that the regions (0-0.77 kHz) and (3.40-11.02 kHz) are the most useful for SID, while those in the region (0.34-3.44 kHz) are best for AID. AID experiments are also performed with intersession variability compensation, which provides the biggest performance gain in the (2.23-5.25 kHz) region. and m.carey} @bham.ac.uk. A. Hanani is currently with the

The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system’s output, we then introduce our health index measure and use it to train the health index forecasting network.

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