nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Survey on Automatic Speech Recognition Systems for Indic Languages

verfasst von : Nandini Sethi, Amita Dev

Erschienen in: Artificial Intelligence and Speech Technology

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

For the past few decades, Automatic Speech Recognition (ASR) has gained a wide range of interest among researchers. From just identifying the digits for a single speaker to authenticating the speaker has a long history of improvisations and experiments. Human’s Speech Recognition has been fascinating problem amongst speech and natural language processing researchers. Speech is the utmost vital and indispensable way of transferring information amongst the human beings. Numerous research works have been equipped in the field of speech processing and recognition in the last few decades. Accordingly, a review of various speech recognition approaches and techniques suitable for text identification from speech is conversed in this survey. The chief inspiration of this review is to discover the prevailing speech recognition approaches and techniques in such a way that the researchers of this field can incorporate entirely the essential parameters in their speech recognition system which helps in overcoming the limitations of existing systems. In this review, various challenges involved in speech recognition process are discussed and what can be the future directives for the researchers of this field is also discussed. The typical speech recognition trials were considered to determine which metrics should be involved in the system and which can be disregarded.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Deep Learning Approaches for Speech Analysis: A Critical Insight

Nächstes Kapitel Analytical Approach for Sentiment Analysis of Movie Reviews Using CNN and LSTM

Thasleema, T.M., Kabeer, V., Narayanan, N.K.: Malayalam vowel recognition based on linear predictive coding parameters and k-NN algorithm. In: Proceedings of international conference on computational intelligence and multimedia applications (ICCIMA 2007), pp. 361–365 (2007)

Sinha, S., Agrawal, S.S., Olsen, J.: Development of Hindi mobile communication text and speech corpus. In: Proceedings of O-COCODSA, pp. 30–35 (2011)

Dutta, K., Sarma, K.K.: Multiple feature extraction for RNN-based Assamese speech recognition for speech to text conversion application. In: Proceedings of the international conference on communications, devices and intelligent systems (CODIS), pp. 600–603 (2012)

Kaur, A., Singh, A.: Optimizing feature extraction techniques constituting phone-based modelling on connected words for Punjabi automatic speech recognition. In: Proceedings of the 2nd International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, pp. 2104–2108 (2016b)

Kadyan, V., Mantri, A., Aggarwal, R.K., Singh, A.: A comparative study of deep neural network-based Punjabi—ASR system. Int. J. Speech Technol. 22(1), 111–119 (2018)CrossRef

Venkateswarlu, R.L.K., Teja, R.R., Kumari, R.V.: Developing efficient speech recognition system for Telugu letter recognition. In: Proceedings of International Conference on Computing, Communication and Applications, pp. 1–6 (2012)

Kumar, A., Dua, M., Choudhary, A.: Implementation and performance evaluation of continuous Hindi speech recognition. In: Proceedings of International Conference on Electronics and Communication Systems (ICECS), pp. 1–5 (2014a)

Bharali, S.S., Kalita, S.K.: Speech recognition with reference to Assamese language using novel fusion technique. Int. J. Speech Technol. 21(2), 251–263 (2018). https://doi.org/10.1007/s10772-018-9501-1CrossRef

Bhowmik, T., Chowdhury, A., Mandal, S.K.D.: Deep neural network-based place and manner of articulation detection and classifcation for Bengali continuous speech. Procedia Comput. Sci. 125, 895–901 (2018)CrossRef

10.

Mohamed, F.K., Lajish, V.L.: Nonlinear speech analysis and modeling for Malayalam vowel recognition. Procedia Comput. Sci. 93, 676–682 (2016)CrossRef

11.

Chellapriyadharshini, M., Tofy, A., Srinivasa, R.K.M., Ramasubramanian, V.: Semi-supervised and active-learning scenarios: efficient acoustic model refinement for a low resource Indian language. In: Computer and Languages, pp. 1041–1045 (2018)

12.

Manjunath, K.E., Sreenivasa Rao, K.: Improvement of phone recognition accuracy using articulatory features. Circ. Syst. Sig. Process. 37(2), 704–728 (2017). https://doi.org/10.1007/s00034-017-0568-8CrossRef

13.

Darekar, R.V., Dhande, A.P.: Emotion recognition from Marathi speech database using adaptive artifcial neural network. Biol. Inspired Cognit. Archit. 23, 35–42 (2018)

14.

Kurian, C., Balakrishnan, K.: Speech recognition of Malayalam numbers. In: Proceedings of the World Congress on Nature and Biologically Inspired Computing, pp. 1475–1479 (2009)

15.

Paul, A.K., Das, D., Kamal, M.: Bangla speech recognition system using LPC and ANN. In: Proceedings of the 7th International Conference on Advances in Pattern Recognition, pp. 171–174 (2009)

16.

Sarma, B.D., Sarmah, P., Lalhminghlui, W., Prasanna, S.M.: Detection of Mizo tones. In: Proceedings of Sixteenth Annual Conference of the International Speech Communication Association, pp. 934–937 (2015)

17.

Sukumar, A.R., Shah, A.F., Anto, P.B.: Isolated question words recognition from speech queries by using artifcial neural networks. In: Proceedings of international conference on computing communication and networking technologies, pp. 1–4 (2010)

18.

Bhuvanagirir, K., Kopparapu, S.K.: Mixed language speech recognition without explicit identifcation of language. Am. J. Sig. Process. 2(5), 92–97 (2012)CrossRef

19.

Das, B., Mandal, S., Mitra, P.: Bengali speech corpus for continuous automatic speech recognition system. In: Proceedings of the International Conference on Speech Database and Assessments, pp. 51–55 (2011)

20.

Sarma, B.D., Sarma, M., Sarma, M., Prasanna, S.R.M.: Development of Assamese phonetic engine: some issues. In: Proceedings of the annual IEEE India Conference (INDICON), pp. 1–6 (2013)

21.

Kumar, S.B.S., Rao, K.S., Pati, D.: Phonetic and prosodically rich transcribed speech corpus in Indian languages: Bengali and Odia. In: Proceedings of International Conference Oriental COCOSDA held Jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE), pp. 1–5 (2013a)

22.

Patil, P.P., Pardeshi, S.A.: Devnagari phoneme recognition system. In: Proceedings of the Fourth International Conference on Advances in Computing and Communications (ICACC), pp. 5–8 (2014b)

23.

Patil, P.P., Pardeshi, S.A.: Marathi connected word speech recognition system. In: Proceedings of the First International Conference on Networks and Soft Computing (ICNSC), pp. 314–318 (2014a)

24.

Hemakumar, G., Punitha, P.: Automatic segmentation of Kannada speech signal into syllables and sub-words: noised and noiseless signals. Int. J. Sci. Eng. Res. 5(1), 1707–1711 (2014)

25.

Patil, V.V., Rao, P.: Detection of phonemic aspiration for spoken Hindi pronunciation evaluation. J. Phon. 54, 202–221 (2016)CrossRef

26.

Dua, M., Aggarwal, R.K., Biswas, M.: Discriminative training using heterogeneous feature vector for Hindi automatic speech recognition system. In: Proceedings of International Conference on Computer and Applications (ICCA), pp. 158–162 (2017)

27.

Kannadaguli, P., Bhat, V.: A comparison of Bayesian and HMM based approaches in machine learning for emotion detection in native Kannada speaker. In: Proceedings of the IEEMA Engineer infinite conference (eTechNxT), pp. 1–6 (2018)

28.

Pulugundla, B., et al.: BUT system for low resource Indian language ASR. In: Interspeech, pp. 3182–3186 (2018)

29.

Dua, M., Aggarwal, R.K., Biswas, M.: Discriminative training using noise robust integrated features and refned HMM modeling. J. Intell. Syst. (2018). https://doi.org/10.1515/jisys-2017-0618CrossRef

30.

Samudravijaya, K., Rao, P.V.S., Agrawal, S.S.: Hindi speech database. In: Proceedings of the International Conference on Spoken Language Processing, pp. 456–464 (2002)

31.

Fathima, N., Patel, T., Mahima, C., Iyengar, A.: TDNN-based multilingual speech recognition system for low resource Indian languages. In: Proceedings of the Inter-speech, pp. 3197–3201 (2018)

32.

Pandey, L., Nathwani, K.: LSTM based attentive fusion of spectral and prosodic information for keyword spotting in Hindi language. In: Interspeech, pp 112–116 (2018)

33.

Pal, M., Roy, R., Khan, S., Bepari, M.S., Basu, J.: PannoMulloKathan: voice enabled mobile app for agricultural commodity price dissemination in Bengali language. In: Interspeech, pp. 1491–1492 (2018)

34.

Patel, T., Krishna, D.N., Fathima, N., Shah, N., Mahima, C., Kumar, D., Iyengar, A.: Development of large vocabulary speech recognition system with keyword search for Manipuri. In: Proceedings of Inter speech (2018). https://doi.org/10.21437/Interspeech.2018-2133

35.

Bhatt, S., Jain, A., Dev, A.: Monophone-based connected word Hindi speech recognition improvement. Sādhanā 46(2), 1–17 (2021). https://doi.org/10.1007/s12046-021-01614-3CrossRef

36.

Agrawal, S.S., Jain, A., Sinha, S.: Analysis and modeling of acoustic information for automatic dialect classification. Int. J. Speech Technol. 19(3), 593–609 (2016). https://doi.org/10.1007/s10772-016-9351-7CrossRef

37.

Bhatt, S., Dev, A., Jain, A.: Effects of the dynamic and energy-based feature extraction on Hindi speech recognition. Recent Adv. Comput. Sci. Commun. 14(5), 1422–1430 (2021)CrossRef

38.

Bhatt, S., Dev, A., Jain, A.: Confusion analysis in phoneme based speech recognition in Hindi. J. Ambient Intell. Humanized Comput. 11(10), 4213–4238 (2020). https://doi.org/10.1007/s12652-020-01703-xCrossRef

39.

Kumari, R., Dev, A., Kumar, A.: Automatic segmentation of Hindi speech into syllable-like units. Int. J. Adv. Comput. Sci. Appl. 11(5), 400–406 (2020)

40.

Bhatt, S., Jain, A., Dev, A.: Feature extraction techniques with analysis of confusing words for speech recognition in the Hindi language. Wireless Pers. Commun. 118(4), 3303–3333 (2021). https://doi.org/10.1007/s11277-021-08181-0CrossRef

41.

Bhatt, S., Jain, A., Dev, A.: Continuous speech recognition technologies—a review. In: Singh, M., Rafat, Y. (eds.) Recent Developments in Acoustics. LNME, pp. 85–94. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5776-7_8CrossRef

42.

Kumari, R., Dev, A., Kumar, A.: An efficient adaptive artificial neural network-based text to speech synthesizer for Hindi language. Multimedia Tools Appl. 80(16), 24669–24695 (2021)CrossRef

43.

Sethi, N., Prajapati, D.K.: Text-independent voice authentication system using MFCC features. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds.) International Conference on Innovative Computing and Communications. AISC, vol. 1165, pp. 567–577. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5113-0_45CrossRef

44.

Sethi, N., Kumar, A., Swami, R.: Automated web development: theme detection and code generation using Mix-NLP. In: ACM International Conference Proceeding Series, p. a45 (2019)

45.

Sethi, D., Sethi, N., Gambhir, P., Anand, R.: E-Pandit: automated voice-based system for religious puja's. In: ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), pp. 174–181, 9197831 (2020)

46.

Sethi, N., Agrawal, P., Madaan, V., Singh, S.K., Kumar, A.: Automated title generation in English language using NLP. Int. J. Control Theor. Appl. 9(Specialissue11), 5159–5168 (2016)

Titel: Survey on Automatic Speech Recognition Systems for Indic Languages
verfasst von: Nandini Sethi
Amita Dev
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Speech Technology
Print ISBN: 978-3-030-95710-0

Electronic ISBN: 978-3-030-95711-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-95711-7_8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner