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2015 | OriginalPaper | Chapter

1. Introduction

Authors : K. Sreenivasa Rao, Dipanjan Nandi

Published in: Language Identification Using Excitation Source Features

Publisher: Springer International Publishing

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Abstract

This chapter introduces the basic goal of language identification (LID) and its impacts on real-life applications. A brief overview of the basic features used for developing LID systems has been given and different categories of LID systems are also discussed here. Eventually, the primary issues in developing LID systems and the major contributions of this book towards solving those issues have been highlighted.

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Literature
1.
go back to reference V.M. Vanishree, Provision for Linguistic Diversity and Linguistic Minorities in India. Master’s thesis. Applied Linguistics, St. Mary’s University College, Strawberry Hill, London, February 2011 V.M. Vanishree, Provision for Linguistic Diversity and Linguistic Minorities in India. Master’s thesis. Applied Linguistics, St. Mary’s University College, Strawberry Hill, London, February 2011
2.
go back to reference F. Runstein, F. Violaro, An isolated-word speech recognition system using neural networks. Circuits Syst. 1, 550–553 (1995) F. Runstein, F. Violaro, An isolated-word speech recognition system using neural networks. Circuits Syst. 1, 550–553 (1995)
3.
go back to reference A. Kocsor, L. Toth, Application of Kernel-based feature space transformations and learning methods to phoneme classification. Appl. Intell. 21, 129–142 (2004)CrossRefMATH A. Kocsor, L. Toth, Application of Kernel-based feature space transformations and learning methods to phoneme classification. Appl. Intell. 21, 129–142 (2004)CrossRefMATH
4.
go back to reference R. Halavati, S.B. Shouraki, S.H. Zadeh, Recognition of human speech phonemes using a novel fuzzy approach. Appl. Soft Comput. 7, 828–839 (2007)CrossRef R. Halavati, S.B. Shouraki, S.H. Zadeh, Recognition of human speech phonemes using a novel fuzzy approach. Appl. Soft Comput. 7, 828–839 (2007)CrossRef
5.
go back to reference T. Hao, M. Chao-Hong, L. Lin-Shan, An initial attempt for phoneme recognition using Structured Support Vector Machine (SVM), in IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 4926–4929 (2010) T. Hao, M. Chao-Hong, L. Lin-Shan, An initial attempt for phoneme recognition using Structured Support Vector Machine (SVM), in IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 4926–4929 (2010)
6.
go back to reference S. Furui, Cepstral analysis techniques for automatic speaker verification. IEEE Trans. Audio Speech Lang. Process. 29(2), 254–272 (1981)CrossRef S. Furui, Cepstral analysis techniques for automatic speaker verification. IEEE Trans. Audio Speech Lang. Process. 29(2), 254–272 (1981)CrossRef
7.
go back to reference D.A. Reynolds, R.C. Rose, Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans. Audio, Speech Lang. Process. 3(1), 4–17 (1995) D.A. Reynolds, R.C. Rose, Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans. Audio, Speech Lang. Process. 3(1), 4–17 (1995)
8.
go back to reference D.A. Reynolds, Speaker identification and verification using gaussian mixture speaker models. Speech Commun. 17, 91–108 (1995)CrossRef D.A. Reynolds, Speaker identification and verification using gaussian mixture speaker models. Speech Commun. 17, 91–108 (1995)CrossRef
9.
go back to reference M. Sugiyama, Automatic language recognition using acoustic features, in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 813–816, May 1991 M. Sugiyama, Automatic language recognition using acoustic features, in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 813–816, May 1991
10.
go back to reference K.S. Rao, S. Maity, V.R. Reddy, Pitch synchronous and glottal closure based speech analysis for language recognition. Int. J. Speech Technol. (Springer) 16(4), 413–430 (2013)CrossRef K.S. Rao, S. Maity, V.R. Reddy, Pitch synchronous and glottal closure based speech analysis for language recognition. Int. J. Speech Technol. (Springer) 16(4), 413–430 (2013)CrossRef
11.
go back to reference J. Balleda, H.A. Murthy, T. Nagarajan, Language identification from short segments of speech. in International Conference on Spoken Language Processing (ICSLP), pp. 1033–1036, October 2000 J. Balleda, H.A. Murthy, T. Nagarajan, Language identification from short segments of speech. in International Conference on Spoken Language Processing (ICSLP), pp. 1033–1036, October 2000
12.
go back to reference V.R. Reddy, S. Maity, K.S. Rao, Recognition of Indian languages using multi-level spectral and prosodic features. Int. J. Speech Technol. (Springer) 16(4), 489–510 (2013)CrossRef V.R. Reddy, S. Maity, K.S. Rao, Recognition of Indian languages using multi-level spectral and prosodic features. Int. J. Speech Technol. (Springer) 16(4), 489–510 (2013)CrossRef
13.
go back to reference S.G. Koolagudi, K. Sreenivasa Rao, Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features. Int. J. Speech Technol. (Springer) 15(3), 495–511 (2012)CrossRef S.G. Koolagudi, K. Sreenivasa Rao, Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features. Int. J. Speech Technol. (Springer) 15(3), 495–511 (2012)CrossRef
14.
go back to reference K. Sreenivasa Rao, S.G. Koolagudi, Emotion Recognition using Speech Features. (Springer, 2012). ISBN 978-1-4614-5142-6 K. Sreenivasa Rao, S.G. Koolagudi, Emotion Recognition using Speech Features. (Springer, 2012). ISBN 978-1-4614-5142-6
15.
go back to reference K. Sreenivasa Rao, S.G. Koolagudi, Robust Emotion Recognition Using Spectral And Prosodic Features. (Springer, 2012). ISBN 978-1-4614-6359-7 K. Sreenivasa Rao, S.G. Koolagudi, Robust Emotion Recognition Using Spectral And Prosodic Features. (Springer, 2012). ISBN 978-1-4614-6359-7
16.
go back to reference S.G. Koolagudi, D. Rastogi, K. Sreenivasa Rao, Spoken language identification using spectral features. Communications in Computer and Information Science (CCIS): Contemporary Computing, vol. 306, (Springer, 2012), pp. 496–497 S.G. Koolagudi, D. Rastogi, K. Sreenivasa Rao, Spoken language identification using spectral features. Communications in Computer and Information Science (CCIS): Contemporary Computing, vol. 306, (Springer, 2012), pp. 496–497
17.
go back to reference D. Neiberg, K. Elenius, K. Laskowski, Emotion recognition in spontaneous speech using GMMs, in Internation Speech Communication and Association (INTERSPEECH), September 2006 D. Neiberg, K. Elenius, K. Laskowski, Emotion recognition in spontaneous speech using GMMs, in Internation Speech Communication and Association (INTERSPEECH), September 2006
18.
go back to reference D. Bitouk, R. Verma, A. Nenkova, Class-level spectral features for emotion recognition. Speech Commun. 52(7), 613–625 (2009) D. Bitouk, R. Verma, A. Nenkova, Class-level spectral features for emotion recognition. Speech Commun. 52(7), 613–625 (2009)
19.
go back to reference K.S. Rao, B. Yegnanarayana, Modeling durations of syllables using neural networks. Comput. Speech Lang. 21, 282–295 (2007)CrossRef K.S. Rao, B. Yegnanarayana, Modeling durations of syllables using neural networks. Comput. Speech Lang. 21, 282–295 (2007)CrossRef
20.
go back to reference A.G. Adami, R. Mihaescu, D.A. Reynolds, J.J. Godfrey, Modeling prosodic dynamics for speaker recognition, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, April 2003 A.G. Adami, R. Mihaescu, D.A. Reynolds, J.J. Godfrey, Modeling prosodic dynamics for speaker recognition, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, April 2003
21.
go back to reference L. Mary, B. Yegnanarayana, Extraction and representation of prosodic features for language and speaker recognition. Speech Commun. 50(10), 782–796 (2008)CrossRef L. Mary, B. Yegnanarayana, Extraction and representation of prosodic features for language and speaker recognition. Speech Commun. 50(10), 782–796 (2008)CrossRef
22.
go back to reference K.S. Rao, S.G. Koolagudi, R.R. Vempada, Emotion recognition from speech using global and local prosodic features. Int. J. Speech Technol. 16(2), 143–160 (2013)CrossRef K.S. Rao, S.G. Koolagudi, R.R. Vempada, Emotion recognition from speech using global and local prosodic features. Int. J. Speech Technol. 16(2), 143–160 (2013)CrossRef
23.
go back to reference K. Sreenivasa Rao, S.G. Koolagudi, Identification of hindi dialects and emotions using spectral and prosodic features of speech. J. Syst. Cybern. Inform. 9(4), 24–33 (2011) K. Sreenivasa Rao, S.G. Koolagudi, Identification of hindi dialects and emotions using spectral and prosodic features of speech. J. Syst. Cybern. Inform. 9(4), 24–33 (2011)
24.
go back to reference J. Yadav, K. Sreenivasa Rao, Emotional-speech synthesis from neutral-speech using prosody imposition, in International Conference on Recent Trends in Computer Science and Engineering (ICRTCSE-2014), Central University of Bihar, Patna, India, 8–9, February 2014 J. Yadav, K. Sreenivasa Rao, Emotional-speech synthesis from neutral-speech using prosody imposition, in International Conference on Recent Trends in Computer Science and Engineering (ICRTCSE-2014), Central University of Bihar, Patna, India, 8–9, February 2014
25.
go back to reference D. Martinez, L. Burget, L. Ferrer, N. Scheffer, i-vector based prosodic system for language identification, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4861–4864, March 2012 D. Martinez, L. Burget, L. Ferrer, N. Scheffer, i-vector based prosodic system for language identification, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4861–4864, March 2012
26.
go back to reference J. Makhoul, Linear prediction: a tutorial review. Proc. IEEE 63(4), 561–580 (1975)CrossRef J. Makhoul, Linear prediction: a tutorial review. Proc. IEEE 63(4), 561–580 (1975)CrossRef
27.
go back to reference B. Yegnanarayana, T.K. Raja, Performance of linear prediction analysis on speech with additive noise, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (1977) B. Yegnanarayana, T.K. Raja, Performance of linear prediction analysis on speech with additive noise, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (1977)
28.
go back to reference C.S. Gupta, S.R.M. Prasanna, B. Yegnanarayana, Autoassociative neural network models for online speaker verification using source features from vowels, in IEEE International Joint Conference Neural Networks May 2002 C.S. Gupta, S.R.M. Prasanna, B. Yegnanarayana, Autoassociative neural network models for online speaker verification using source features from vowels, in IEEE International Joint Conference Neural Networks May 2002
29.
go back to reference D. Pati, S.R.M. Prasanna, Subsegmental, segmental and suprasegmental processing of linear prediction residual for speaker information. Int. J. Speech Technol. (Springer) 14(1), 49–63 (2011)CrossRef D. Pati, S.R.M. Prasanna, Subsegmental, segmental and suprasegmental processing of linear prediction residual for speaker information. Int. J. Speech Technol. (Springer) 14(1), 49–63 (2011)CrossRef
30.
go back to reference D. Pati, D. Nandi, K. Sreenivasa Rao, Robustness of excitation source information for language independent speaker recognition, in 16th International Oriental COCOSDA Conference, Gurgoan, India, November 2013 D. Pati, D. Nandi, K. Sreenivasa Rao, Robustness of excitation source information for language independent speaker recognition, in 16th International Oriental COCOSDA Conference, Gurgoan, India, November 2013
31.
go back to reference D. Pati, S.R.M. Prasanna, A comparative study of explicit and implicit modelling of subsegmental speaker-specific excitation source information. Sadhana (Springer) 38(4), 591–620 (2013) D. Pati, S.R.M. Prasanna, A comparative study of explicit and implicit modelling of subsegmental speaker-specific excitation source information. Sadhana (Springer) 38(4), 591–620 (2013)
32.
go back to reference A. Bajpai, B. Yegnanarayana, Exploring features for audio clip classification using LP residual and AANN models, in International Conference on Intelligent Sensing and Information Processing, pp. 305–310, January 2004 A. Bajpai, B. Yegnanarayana, Exploring features for audio clip classification using LP residual and AANN models, in International Conference on Intelligent Sensing and Information Processing, pp. 305–310, January 2004
33.
go back to reference K.S. Rao, S.G. Koolagudi, Characterization and recognition of emotions from speech using excitation source information. Int. J. Speech Technol. (Springer) 16, 181–201 (2013)CrossRef K.S. Rao, S.G. Koolagudi, Characterization and recognition of emotions from speech using excitation source information. Int. J. Speech Technol. (Springer) 16, 181–201 (2013)CrossRef
34.
go back to reference A.V. Singh, J. Mukhopadhyay, K. Sreenivasa Rao, K. Viswanath, Classification of infant cries using dynamics of epoch features. J. Intell. Syst. 22(3), 253–267 (2013) A.V. Singh, J. Mukhopadhyay, K. Sreenivasa Rao, K. Viswanath, Classification of infant cries using dynamics of epoch features. J. Intell. Syst. 22(3), 253–267 (2013)
35.
go back to reference A.V. Singh, J. Mukhopadyay, S.B.S. Kumar, K. Sreenivasa Rao, Infant cry recognition using excitation source features, in IEEE INDICON, Mumbai, India, December 2013 A.V. Singh, J. Mukhopadyay, S.B.S. Kumar, K. Sreenivasa Rao, Infant cry recognition using excitation source features, in IEEE INDICON, Mumbai, India, December 2013
36.
go back to reference S.R.M. Prasanna, C.S. Gupta, B. Yegnanarayana, Extraction of speaker-specific excitation information from linear prediction residual of speech. Speech Commun. 48, 1243–1261 (2006)CrossRef S.R.M. Prasanna, C.S. Gupta, B. Yegnanarayana, Extraction of speaker-specific excitation information from linear prediction residual of speech. Speech Commun. 48, 1243–1261 (2006)CrossRef
Metadata
Title
Introduction
Authors
K. Sreenivasa Rao
Dipanjan Nandi
Copyright Year
2015
DOI
https://doi.org/10.1007/978-3-319-17725-0_1