Abstract
Spoken language has been the most natural mode of communication since a long time. In today’s world, the advancement of telecommunication technology has taken communication to the next level. This acts as motivation for an automatic speech recognition (ASR) process. As research in ASR progresses, the development of language identification would be necessary for any multi-lingual ASR system. This problem is highly pertinent to India due to the existence of a wide diversity of languages, each having hundreds of dialects. In this chapter, we propose a spoken language identification framework for the recognition of the six most widely used spoken languages in India namely, English, Hindi, Bangla, Marathi, Tamil and Telugu. Firstly, the pre-processing is performed on an input audio clip, which focuses on removing its silent frames. This, in turn, increases its probability of recognition as redundant and non-informative information gets eliminated. The silence removal parameters are optimized in order to achieve the best performance. Secondly, the Mel Frequency Cepstral Coefficient (MFCC) features are extracted from the speech signals. Using these features, a Support Vector Machine (SVM) classifier is trained, which has been established as a superior classifier, due to its large-margin classification property, and its ability to classify complex and non-linear data. The proposed framework is tested on a standard IndicTTS speech database. SVM is trained for 13 (static), 26 (static + delta) and 39 (static + delta + delta-delta) MFCC features. It is found that the best results are achieved using only 13 static features, and addition of delta and delta-delta features decreases the performance. Maximum accuracy obtained is 60.33% for the above-mentioned 6 languages, and 89.33% for only Bangla, English and Hindi.
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Biswas, M., Rahaman, S., Kundu, S., Singh, P.K., Sarkar, R. (2021). Spoken Language Identification of Indian Languages Using MFCC Features. In: Kumar, P., Singh, A.K. (eds) Machine Learning for Intelligent Multimedia Analytics. Studies in Big Data, vol 82. Springer, Singapore. https://doi.org/10.1007/978-981-15-9492-2_12
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