Identification/segmentation of indian regional languages with singular value decomposition based feature embedding

Abstract Language identification (LID) is identifying a language in a given spoken utterance. Language segmentation is equally important as language identification where language boundaries can be spotted in a multi-language utterance. Language identification could be a trivial front-end process for real-time mixed-speech recognition applications. India is a multilingual country and mixing two languages in a single conversation is very usual. In this paper, we have experimented with two schemes for language identification in Indian regional language context as very few works have been done. Singular value-based feature embedding is used for both of the schemes. In the first scheme, the singular value decomposition (SVD) is applied to the n-gram utterance matrix and in the second scheme, SVD is applied to the difference supervector matrix space. We have observed that in both the schemes, 55–65% singular value energy is sufficient to capture the language context. We have also seen how these two schemes are preserving language context. In n-gram based feature representation, we have seen that different skipgram models capture different language context. We have observed that for short test duration, supervector based feature representation is better but with a longer duration test signal, n-gram based feature performed better. We have also extended our work to explore language-based segmentation, where we have seen that segmentation accuracy of four language group with ten language training model, scheme-1 has performed well but with same four language training model, scheme-2 has shown better accuracy. In a multilingual language setup, the language-based identification and segmentation will be useful to identify the language as well as the duration of its presence. Further, the language-specific model can be used to identify the speech.