Identification/Segmentation of Indian Regional Languages with Singular Value Decomposition based Feature Embedding
This addresses the problem of language processing for Indian regional languages, which is an incremental contribution as it applies existing methods to a new data context.
The paper tackled language identification and segmentation for Indian regional languages using singular value decomposition-based feature embedding, achieving 55-65% singular value energy capture and showing that supervector-based features perform better for short test durations while n-gram features excel with longer durations.
language identification (LID) is identifing a language in a given spoken utterance. Language segmentation is equally inportant as language identification where language boundaries can be spotted in a multi language utterance. In this paper, we have experimented with two schemes for language identification in Indian regional language context as very few works has been done. Singular value based feature embedding is used for both of the schemes. In first scheme, the singular value decomposition (SVD) is applied to the n-gram utterance matrix and in the second scheme, SVD is applied on 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. 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 outperformed scheme-1