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Previous accent classification research focused mainly on detecting accents with pure acoustic information without recognizing accented speech. This work combines phonetic knowledge such as vowels with acoustic information to build Guassian Mixture Model (GMM) classifier with Perceptual Linear Predictive (PLP) features, optimized by Hetroscedastic Linear Discriminant Aanlysis (HLDA). With in-put about 20-second accented speech, this system achieves classification rate of 51% on a 7-way classification system focusing on the major types of accents in English, which is competitive to the state-of-the-art results in this field.

Nuclear Quadrupole Resonance (NQR) technology for the detection of explosives is of crucial importance in an increasing number of applications. For landmine detection, NQR has proven to be highly effective if the NQR sensor is not exposed to radio frequency interference (RFI). Since strong nonstationary RFI in the field is unavoidable, a robust detection method is required. With the aid of reference antennas, a frequency domain LMS algorithm is applied to cancel the RFI in field data. An average power detector based on power spectral estimation algorithms is proposed and performance using both the periodogram and MUSIC algorithms is evaluated. The detection performance has been compared with that of a non-adaptive Bayesian detector. The experimental results show that, unlike the non-adaptive Bayesian detector, the average power detector provides perfect detection capability if the data segments involved in the collection process are sufficiently long. 0-7803-7536-X/$17.00 (C) 2002 IEEE 1552a 1575

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