Language accent classification in American English
Speech Communication
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
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ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Accent Classification in Speech
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Fast accent identification and accented speech recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
The effect of listener accent background on accent perception and comprehension
EURASIP Journal on Audio, Speech, and Music Processing
Accent classification using support vector machine and hidden Markov model
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
RASTA-PLP speech analysis technique
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Accent classification for speech recognition
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Speech Compression by Polynomial Approximation
IEEE Transactions on Audio, Speech, and Language Processing
Dialect/Accent Classification Using Unrestricted Audio
IEEE Transactions on Audio, Speech, and Language Processing
Advances in phone-based modeling for automatic accent classification
IEEE Transactions on Audio, Speech, and Language Processing
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This paper presents a classification model for regional accents of Persian. The model is based on a combination of the conventional speech coding and pattern recognition techniques. In this study, the well-known multilayer perceptron plays the role of the classifier. Moreover, a wide variety of speech coding techniques is utilized for feature extraction. Among them, we determine the robust and optimum features for this task by comparing the classification performance. The method is validated on a corpus containing recordings from ten speakers, five males and five females, for each accent. Results show that perceptual linear predictive (PLP), relative spectral transform PLP (Rasta PLP), and linear predictive coefficient (LPC) perform well under both clean and noisy conditions.