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Signal language8/7/2023 ![]() and Sethu, V., "Language identification: A tutorial", IEEE Circuits and Systems Magazine, Vol. Ambikairajah, E., Li, H., Wang, L., Yin, B.and Kaushik, P., "Literature survey of statistical, deep and reinforcement learning in natural language processing", in 2017 International Conference on Computing, Communication and Automation (ICCCA), IEEE, (2017), 350-354, DOI: 10.1109/CCAA.2017.8229841 and Li, M., "Insights in-to-end learning scheme for language identification", in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, (2018), 5209-5213, DOI: 10.21437/Interspeech.2019-1386 The proposed method achieved 72.20% accuracy for language identification. The proposed method was tested on our dataset with 31000 texts from 31 different languages. Also, a multilayer perceptron neural network is used to classify the extracted features. Third, each cluster is decomposed into 32 sub-bands using a Wavelet packet, and 32 features are extracted from each sub-band. Second, to group similar languages, the obtained series are clustered. First, the texts are converted to time series using UTF-8 codes. ![]() ![]() We proposed a dictionary independent method consisting of three main steps, I) preprocessing, II) clustering and finally III) classification. Although several research and commercial software have been developed to identify text language, they need a standard dictionary for each language. The signature can be extracted using signal processing techniques via converting texts into time series. The sequence of characters in a stream provides a signature to recognize the language without understanding its meaning. Sequence of characters in a word and the order of words in stream identify the language. In this paper, a signal processing method for Language Identification is proposed. Language identification is a critical step prior to any natural language processing.
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