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:  2017
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:  66
:   .., .. / . 66. .: , 2017. .6-24. URL: https://doi.org/10.25728/ubs.2017.66.1
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(.):  language identification, neural networks, speech prosodic features, broad phonetic categories
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(.):  We study the language identification problem using prosodic features. Prosodic features such as melody, rhythm, timbre and others are difficult to formalize mathematically. Two algorithms for a complex description of prosodic features are proposed in the paper. The first is based on the broad phonetic categories, and the second is based on the cross-correlation of the speech melody and the short-term energy sequence. The fundamental frequency was estimated by MELP algorithm. The performance of the proposed algorithms was evaluated experimentally on a database of speech recordings obtained from Internet and therefore encoded by low-bitrate vocoders. The database includes ten different languages. The proposed algorithms provide a feature description and a multi-layer neural network was used as a language classifier. Both algorithms show satisfactory classification performance, but the broad phonetic categories approach performs slightly better than the cross-correlation function. These algorithms can be applied to a speech signal processed by low-bitrate vocoders without decoding to the original signal.

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