Continuous Speech Recognition and Identification of the Speaker System

  • Diego Guffanti
  • Danilo Martínez
  • José Paladines
  • Andrea Sarmiento
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)


Currently speech recognition and speaker identification based on a biometric parameter such as voice have been treated as two different worlds and in the market there are no integrated applications of these systems. The design of a system could mean a great contribution to the development of personalized commands, in the area of home automation and robotics, thanks to the availability of the message and the identification of the speaker. Therefore, the development of an integrated biometric voice system is proposed, based on a single voice sample for the identification of the speaker and the message. We use GOOGLE SPEECH API, as a voice text translation tool, and Mel Frequency Cepstral Coefficients or MFCCs extracted from voice signal to identify speakers voice. Functional tests were carried with 50 randomly users, in the end of the study results show 96.4% efficiency in identification, demonstrating efficiency using MFCCs in speaker’s automatic recognition and verifying the use of GOOGLE SPEECH API as a fast, accurate and robust translation tool.


Mel Frequency Cepstral Coefficients GOOGLE SPEECH API RAH RAL 


  1. 1.
    Leu, F.Y., Lin, G.L.: An MFCC-based speaker identification system. In: Proceedings of the International Conference on Advanced Information Networking and Applications, AINA, pp. 1055–1062 (2017)Google Scholar
  2. 2.
    HTK Speech Recognition Toolkit.
  3. 3.
    Povey, D., Ghoshal, A.: The Kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding, Hilton Waikoloa Village (2011)Google Scholar
  4. 4.
    API Speech: reconocimiento de voz | Google Cloud Platform.
  5. 5.
    Introduction to Audio Encoding | Google Cloud Speech API | Google Cloud Platform.
  6. 6.
    Juang, B.H., Chen, T.: The past, present and future of speech processing. IEEE Signal Process. Mag. 15, 24–48 (1998). B.H. Juang (ed.)CrossRefGoogle Scholar
  7. 7.
    Davis, S.B., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Sig. Process. 28(4), 357–366 (1980)CrossRefGoogle Scholar
  8. 8.
    Khalifa, O., Islam, R., Khan, S., Faizal, M., Dol, D.: Text independent automatic speaker recognition. In: 3rd International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, pp. 561–564 (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Diego Guffanti
    • 1
  • Danilo Martínez
    • 2
  • José Paladines
    • 3
  • Andrea Sarmiento
    • 4
  1. 1.Universidad Tecnológica Equinoccial UTESanto DomingoEcuador
  2. 2.Universidad de las Fuerzas Armadas ESPESangolquíEcuador
  3. 3.Technical Sciences FacultyUniversidad Estatal del Sur de ManabíJipijapaEcuador
  4. 4.Universidad Católica de Cuenca UCACUECuencaEcuador

Personalised recommendations