Abstract
The main focus of this paper is towards the development of a large vocabulary Telugu speech database. Telugu is a low resource language where there exists no standardized database for building the speech recognition system (ASR). The database consists of neutral speech samples collected from 100 speakers for building the Telugu ASR system and it was named as IIIT-H Telugu speech corpus. The speech and text corpus design and the procedure followed for the collection of the database have been discussed in detail. The preliminary ASR system results for the models built in this database are reported. The architectural choices of deep neural networks (DNNs) play a crucial role in improving the performance of ASR systems. ASR trained with hybrid DNNs (DNN-HMM) with more hidden layers have shown better performance over the conventional GMMs (GMM-HMM). Kaldi tool kit is used for building the acoustic models required for the ASR system.
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The authors would like to thank TCS for partially funding the first author for his PhD programme.
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Vegesna, V.V.R., Gurugubelli, K., Vydana, H.K., Pulugandla, B., Shrivastava, M., Vuppala, A.K. (2017). DNN-HMM Acoustic Modeling for Large Vocabulary Telugu Speech Recognition. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_19
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