Abstract
The paper presents the identification of a speaker with the help of natural and whisper speech signal. The speaker identification through whisper is also useful in cases where the speaker is unable to speak, or speaker wants to hide his/her identity. However, the changes in whispering speech due to the vocal effort have several challenges in maintaining system accuracy. The accuracy of speaker identification is calculated with Mel Frequency Cepstral Coefficient (MFCC) algorithm and Exponential Frequency Cepstral Coefficient (EFCC) algorithm. MFCC and EFCC are considered for feature extraction. All the samples are clustered using K-means algorithm and Gaussian Mixture Model for feature classification. GMM is containing mean, variance, and weight which are modeling parameters. Here Expectation–Maximization algorithm is used for testing the samples and reestimate the parameters. Finally, GMM algorithm recognizes the speaker that exactly matches for a given database. The algorithms are implemented on MATLAB tool, and the results are also verified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fan X, Hansen, JHL (2008) Speaker identification for whispered speech based on frequency warping and score competition. In: Ninth annual conference of the international speech communication association, pp 1313–1316
Shukla UP., Patel NB, Joshi AM (2013) A survey on recent advances in speech compressive sensing. In: 2013 international multi-conference on automation, computing, communication, control and compressed sensing (iMac4s), pp 276–280. IEEE
Bahdanau D, Chorowski J, Serdyuk D, Brakel P, Bengio Y (2016) End-to-end attention-based large vocabulary speech recognition. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4945–4949
Xiong W, Droppo J, Huang X, Seide F, Seltzer M, Stolcke A, Yu D, Zweig G (2017) The Microsoft 2016 conversational speech recognition system. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 5255–5259. IEEE
Bhadoria BS, Shukla U, Joshi AM (2014) Comparative analysis of basis & measurement matrices for non-speech audio signal using compressive sensing. In: 2014 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–5. IEEE
Xiong W, Droppo J, Huang X, Seide F, Seltzer M, Stolcke A, Yu D, Zweig G (2016) Achieving human parity in conversational speech recognition. arXiv preprint arXiv:1610.05256
Sak H, Senior A, Rao K, Irsoy O, Graves A, Beaufays F, Schalkwyk J (2015) Learning acoustic frame labeling for speech recognition with recurrent neural networks. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4280–4284. IEEE
Bimbot F, Bonastre JF, Fredouille C, Gravier G, Magrin Chagnolleau I, Meignier S, Merlin T, Ortega-Garcia J, Petrovska Delacreetaz D, Reynolds DA (2004) A tutorial on text-independent speaker verification. EURASIP J Signal Process 4. Hindawi Publishing Cor-poration, pp 430–451
ShuklaUP, Patel NB, Joshi AM (2013) Analysis of music signal compression with compressive sensing. Int J Comput Appl 70(26)
Bou-Ghazale SE, Hansen JHL (2000) A comparative study of traditional and newly proposed features for recognition of speech under stress. IEEE Trans Speech Audio Process 8(4):429–442. Prentice Hall, Upper Saddle River, NJ, USA
Zhou G, Hansen JHL, Kaiser JF (2001) Nonlinear feature based classification of speech under stress. IEEE Trans Speech Audio Process 9(3):201–216
Li X (2004) Reconstruction of speech from Chinese Whispers. PhD, Nanjing University, China; Ito T, Takeda K Itakura F (2005) Analysis and recognition of whispered speech. Speech Commun 45:139–152
Jin Q, Jou SCS, Schultz T (2007) Whispering speaker identification. In: 2007 IEEE international conference on multimedia and expo, pp 1027–1030. IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, A., Joshi, A.M. (2020). Speaker Identification Through Natural and Whisper Speech Signal. In: Janyani, V., Singh, G., Tiwari, M., d’Alessandro, A. (eds) Optical and Wireless Technologies . Lecture Notes in Electrical Engineering, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-13-6159-3_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-6159-3_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6158-6
Online ISBN: 978-981-13-6159-3
eBook Packages: EngineeringEngineering (R0)