Abstract
Dialect Identification (DID) is a particular case of general Language Identification (LID). Due to the high similarity of dialects and similar phonetic features in adjacent areas, DID is a more challenging problem. Long Short-Term Memory (LSTM) networks tend to be used and do well in LID tasks in recent years, but do not have a good performance on DID tasks. In this paper, NOAA (New One-against-all) binary classifier based on OAA (One-against-all) binary classifier obtained proposed, and a new dialect recognition method was combining NOAA with LSTM networks was offered under the guidance of Chinese humanities. The new approach achieves better performance on a DID task than a single LSTM network. The experiment was conducted based on six major dialects in China, and trained under the acoustic characteristics of log Mel-scale filter banks energies (FBANK). Experimental results on six dialects recognition tasks indicate that the accuracy of the new method is higher than that of a single LSTM network.
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Acknowledgment
This research was financially supported by the key Technology R&D Program projects of Henan Province (172102210003), College Students Innovation and Entrepreneurship Training Program of Henan Province (S201810459069) and College Students Innovation and Entrepreneurship Training Program of Zhengzhou University (201810459069).
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Ye, S., Li, C., Zhao, R., Wu, W. (2019). NOAA-LSTM: A New Method of Dialect Identification. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_2
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DOI: https://doi.org/10.1007/978-3-030-24274-9_2
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