Abstract
The database is an essential key element for speech recognition research. This research describes the development of the SCEHMA speech database dedicated to advance speech recognition applications in Hindi, English, Marathi and Arabic languages. The SCEHMA corpus is a collection of isolated word and continuous sentences of speech. For the application domain of agriculture, polyclinic and general-purpose speech recognition in Marathi language 28420 isolated words and 17470 sentences are collected from 300 male and 200 female subjects of 22–30 age groups. The corpus consists of 900 sentences in the Hindi language for accent recognition domain collected from 18 male and 12 female of 18–30 age groups. The English speech corpus was collected from 22–30 age groups of 750 isolated words and 750 sentences from 12 male and 3 female of age group 22–30 for the general domain. The Arabic speech corpus contains 4520 words and 40 sentences from 12 male and 9 female of 18–30 age groups for recognition domain. To achieve a high quality of speech corpus, the recording took place in 10 by 10 office room without a noisy sound environment. The speech utterances were recorded in 16 kHz in three recordings medium, a headset, desktop mounted microphone and Mobile phone. The data was recorded in the morning, and evening session in the room temperature and normal humidity. Speaker was asked to sit in front of the microphone with a distance of about 12–15 cm. The database is collected as per LDCIL protocol and the corpus is transcript through Google Unicode editor. Praat is used for corpus labeling and annotation. The total size of the SCEHMA corpus is 33690 isolated words and 19160 continuous sentences. The corpus will be made available to the scientific community for agricultural, polyclinic, medical, accent recognition, age group identification, gender recognition, and general-purpose recognition system after the transcription and annotation.
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References
Schultz, T., Weibel, A.: Language independent and language adaptive acoustic modeling for speech recognition. Speech Commun. 35(1–2), 31–51 (2001)
Murthy, H.A., et al.: Building unit selection speech synthesis in Indian languages: an initiative by an Indian consortium. In: Proceedings of COCOSDA, Kathmandu, Nepal (2010)
English Language [Online]. http://en.wikipedia.org/wiki/English_language. Accessed Dec 2018
Hindi Language [Online]. http://en.wikipedia.org/wiki/Hindustani_phonology. Accessed Dec 2018
Singh, S.P., et al.: Building large vocabulary speech recognition systems for Indian languages. In: International Conference on Natural Language Processing, pp. 245–254 (2004)
Marathi CIIL corpus [source]. http://tdil.mit.gov.in/corpora/achcorpora
Gaikwad, S., Gawali, B., Mehrotra, S.: Creation of Marathi speech corpus for automatic speech recognition. In: International Conference Oriental COCOSDA held Jointly with the 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE) (2013)
Al-Zabibi, M.: An acoustic–phonetic approach in automatic Arabic speech recognition. The British Library in Association with UMI (1990)
Gaikwad, S.K., Gawali, B., Yannawar, P.: A review on speech recognition technique. Int. J. Comput. Appl. 10, 16–24 (2010)
Godin, C., Lockwood, P.: DTW schemes for continuous speech recognition: a unified view. Comput. Speech Lang. 3(2), 169–198 (1989)
Yuliani, A.R., Sustika, R., Yuwana, R.S., Pardede, H.F.: Feature transformations for robust speech recognition in reverberant conditions. In: 2017 International Conference on Computer Control Informatics and its Applications (IC3INA), pp. 57–62 (2017)
Prasetio, M.D., Hayashida, T., Nishizaki, I., Sekizaki, S.: Structural optimization of deep belief network theorem for classification in speech recognition. In: 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA), pp. 121–128 (2017)
Khara, S., Singh, S., Vir, D.: A comparative study of the techniques for feature extraction and classification in stuttering. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 887–893 (2018)
Gawali, B.W., Gaikwad, S., Yannawar, P., Mehrotra, S.C.: Marathi isolated word recognition system uses MFCC and DTW features. ACEE Int. J. Inf. Technol. 1(1), 21–24 (2011)
Praat Tutorial [Online]. http://www.stanford.edu/dept/linguistics/corpora/material/PRAAT_workshop_manual_v421.pdf
Praat Tutorial [online] source. http://www.fon.hum.uva.nl/praat
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Gaikwad, S., Gawali, B., Basil, M. (2020). SCEHMA: Speech Corpus of English, Hindi, Marathi and Arabic Language for Advance Speech Recognition Development. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_10
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