Keywords

Memory-based learning, Cantonese tone recognition, Feature selection and extraction, Neural networks, Optimization and experiments

Abstract

This paper introduces memory-based learning as a viable approach for Cantonese tone recognition. The memorybased learning algorithm employed here outperforms other documented current approaches for this problem, which is based on neural networks. Various numbers of tones and features are modeled to find the best method for feature selection and extraction. To further optimize this approach, experiments are performed to isolate the best feature weighting method, the best class voting weights method, and the best number of k-values to implement. Results and possible future work are discussed.

Original Publication Citation

Michael Emonts and Deryle Lonsdale. (2003). A memory-based approach to Cantonese tone recognition, Proceedings of the 8th European Conference on Speech Communication and Technology (EuroSpeech 2003), Geneva, Switzerland; ISCA, pp. 2305-2308.