Skip to main content

Advertisement

Log in

Development of speech recognition system for remote vocal music teaching based on Markov model

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

With the popularization of smart homes, car audio systems and various speech recognition software, speech recognition systems have gradually entered people’s sights and are favored by most users because of their practicability and accuracy. Cognition is an important interface for human–computer interaction. It will become a research focus in the field of artificial intelligence. It plays an important role in cultivating the basic characteristics of music and cultivating students’ interest in music, and vocal music teaching. Teaching traditional vocal music education to students is in the form of classrooms, such as vocal music, arrangement, and bel canto. The disadvantage is the lack of communication between the classroom and teachers and students. On the other hand, the development of Internet technology provides a new teaching method for traditional vocal music teaching and provides a network infrastructure for building a vocal teaching system platform. Therefore, this article provides a preliminary construction of a remote vocal music education platform by combining vocal music education with Internet technology. The remote audio and video training system is a complex and relatively large project with multiple functions and is to introduce important functions in this system. At the same time, register and log in to the remote voice and video implementation requirements and system functions, respectively, to realize functions such as video training and video-on-demand training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Data will be made available on request.

References

  • Al-Atik L, Abrahamson N (2010) An improved method for nonstationary spectral matching. Earthq Spectra 26(3):601–617

    Article  Google Scholar 

  • Awwalu J, Bakar AA, Yaakub MR (2019) Hybrid N-gram model using Naïve Bayes for classification of political sentiments on Twitter. Neural Comput Appl 31(12):9207–9220

    Article  Google Scholar 

  • Butun I, Morgera SD, Sankar R (2013) A survey of intrusion detection systems in wireless sensor networks. IEEE Commun Surv Tutorials 16(1):266–282

    Article  Google Scholar 

  • Devi VA, Suganya MV (2016) An analysis on types of speech recognition and algorithms. Int J Comput Sci Trends Technol (IJCST) 4(2):350–355

    Google Scholar 

  • Healy EW, Yoho SE, Wang Y, Wang D (2013) An algorithm to improve speech recognition in noise for hearing-impaired listeners. J Acoust Soc Am 134(4):3029–3038

    Article  Google Scholar 

  • Hodgson T, Coiera E (2016) Risks and benefits of speech recognition for clinical documentation: a systematic review. J Am Med Inform Assoc 23(e1):e169–e179

    Article  Google Scholar 

  • Kallio AA, Heimonen M (2019) A toothless tiger? Capabilities for indigenous self-determination in and through Finland’s extracurricular music education system. Music Educ Res 21(2):150–160

    Article  Google Scholar 

  • Kurzekar PK, Deshmukh RR, Waghmare VB, Shrishrimal PP (2014) A comparative study of feature extraction techniques for speech recognition system. Int J Innov Res Sci Eng Technol 3(12):18006–18016

    Article  Google Scholar 

  • Li K, Wang X, Xu Y, Wang J (2016) Lane changing intention recognition based on speech recognition models. Transport Res Part c: Emerg Technol 69:497–514

    Article  MathSciNet  Google Scholar 

  • Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  • Lu JI (2014) Exploration of the use and reform of vocal music teaching materials in Chinese art colleges. Can Soc Sci 10(5):48–51

    Google Scholar 

  • Maleh Y, Ezzati A, Qasmaoui Y, Mbida M (2015) A global hybrid intrusion detection system for wireless sensor networks. Procedia Comput Sci 52:1047–1052

    Article  Google Scholar 

  • Principi E, Squartini S, Bonfigli R et al (2015) An integrated system for voice command recognition and emergency detection based on audio signals. Expert Syst Appl 42(13):5668–5683

    Article  Google Scholar 

  • Salman AD, Khalaf OI, Abdulsahib GM (2019) An adaptive intelligent alarm system for wireless sensor network. Indonesian J Electr Eng Comput Sci 15(1):142–147

    Article  Google Scholar 

  • Samek W, Binder A, Montavon G et al (2016) Evaluating the visualization of what a deep neural network has learned. IEEE Trans Neural Netw Learn Syst 28(11):2660–2673

    Article  MathSciNet  Google Scholar 

  • Sun J (2020) Research on resource allocation of vocal music teaching system based on mobile edge computing. Comput Commun 160:342–350

    Article  Google Scholar 

Download references

Funding

This paper was supported by The Humanities and Social Science Research Planning Fund Project of the Ministry of Education in China 2020: Research on "Environmental Music" Cultural Mission.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu Xia.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, F., Xia, Y. Development of speech recognition system for remote vocal music teaching based on Markov model. Soft Comput 27, 10237–10248 (2023). https://doi.org/10.1007/s00500-023-08277-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-08277-8

Keywords

Navigation