Abstract
Speaker identification is the method of human voice identifying with the help of artificial intelligence (AI) method. The technology of speaker identification is broadly utilized in voice recognition, secure, surveillance, electronic voice eavesdropping, and the verification of identity. In the existing methods, it does not provide the sufficient accuracy and robustness of the speech signal. To overcome these issues, an efficient Speaker Identification framework based on Mask region based convolutional neural network (Mask R-CNN) classifier parameter optimized using Hosted Cuckoo Optimization (HCO) is proposed in this manuscript. The objective of the proposed method is “to increase the accuracy and to improve the robustness of the signal”. Initially, the input speech signals are taken from the real time dataset. From the input speech signal, there are four types of the features are extracted, they are Mel Frequency Differential Power Cepstral Coefficients (MFDPCC), Gamma tone Frequency Cepstral Coefficients (GFCC), Power Normalized Cepstral Coefficients (PNCC) and Spectral entropy for improving the robustness of the signal. Then, the speaker ID is classified by using the Mask R-CNN classifier. Similarly, the Mask R-CNN classifier parameters are optimized by using the HCO algorithm. This method is relevant in the real time application, such as telephone banking and the fax mailing. The simulation is executed in MATLAB. The simulation results shows that the proposed Mask-R-CNN-HCO method attains accuracy of 24.16%, 32.18%, 28.43%, 36.4%, 33.26%, Sensitivity of 37.68%, 33.80%, 24.16%, 32.18%, 28.43%, Precision of 35.88%, 24.16%, 32.18%, 28.43%, 26.77% higher than the existing methods, such as Automatic Classification of speaker identification using K-Nearest Neighbors algorithm (KNN), classification of speaker identification using multiclass support vector machine(MCSVM), classification of speaker identification using Gaussian Mixture Model–Convolutional Neural Network (GMMCNN) classifier, classification of speaker identification using Deep neural network (DNN) and classification of speaker identification using Gaussian Mixture Model–deep Neural Network (GMMDNN) classifier.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Code accessibility
Not applicable.
References
Abd El-Moneim S, Nassar MA, Dessouky MI, Ismail NA, El-Fishawy AS, Abd El-Samie FE (2020) Text-independent speaker recognition using LSTM-RNN and speech enhancement. Multimed Tools Appl 79(33):24013–24028
Bisio I, Garibotto C, Grattarola A, Lavagetto F, Sciarrone A (2018) Smart and robust speaker recognition for context-aware in-vehicle applications. IEEE Trans Veh Technol 67(9):8808–8821
Chen C, Wang W, He Y, Han J (2019) A bilevel framework for joint optimization of session compensation and classification for speaker identification. Digit Signal Process 89:104–115
Devi KJ, Thongam K (2019) Automatic speaker recognition with enhanced swallow swarm optimization and ensemble classification model from speech signals. J Ambient Intell Humaniz Comput 1–4
El Ayadi M, Hassan AK, Abdel-Naby A, Elgendy OA (2017) Text-independent speaker identification using robust statistics estimation. Speech Commun 92:52–63
Geravanchizadeh M, Forouhandeh E, Bashirpour M (2021) Feature compensation based on the normalization of vocal tract length for the improvement of emotion-affected speech recognition. EURASIP J Audio Speech Music Process 1:1–9
Greenberg CS, Mason LP, Sadjadi SO, Reynolds DA (2020) Two decades of speaker recognition evaluation at the national institute of standards and technology. Comput Speech Lang 60:101032
Han JH, Bae KM, Hong SK, Park H, Kwak JH, Wang HS, Joe DJ, Park JH, Jung YH, Hur S, Yoo CD (2018) Machine learning-based self-powered acoustic sensor for speaker recognition. Nano Energy 53:658–665
Hourri S, Kharroubi J (2020) A deep learning approach for speaker recognition. Int J Speech Technol 23(1):123–131
Hourri S, Nikolov NS, Kharroubi J (2021) Convolutional neural network vectors for speaker recognition. Int J Speech Technol 24(2):389–400
Jagdale SM, Shinde AA, Chitode JS (2020) Robust speaker recognition based on low-level-and prosodic-level-features. In: Advances in data sciences, security and applications. Springer, Singapore, pp 267–274
Jahangir R, Teh YW, Memon NA, Mujtaba G, Zareei M, Ishtiaq U, Akhtar MZ, Ali I (2020) Text-independent speaker identification through feature fusion and deep neural network. IEEE Access 8:32187–32202
Jessen M, Bortlík J, Schwarz P, Solewicz YA (2019) Evaluation of Phonexia automatic speaker recognition software under conditions reflecting those of a real forensic voice comparison case (forensic_eval_01). Speech Commun 111:22–28
Kumaran U, Rammohan SR, Nagarajan SM, Prathik A (2021) Fusion of mel and gammatone frequency cepstral coefficients for speech emotion recognition using deep C-RNN. Int J Speech Technol 24(2):303–314
Kwon S (2021) Att-Net: enhanced emotion recognition system using lightweight self-attention module. Appl Soft Comput 102:107101
Madhavi MC, Patil HA (2019) Vocal Tract Length Normalization using a Gaussian mixture model framework for query-by-example spoken term detection. Comput Speech Lang 58:175–202
Mellal MA, Frik A, Boutiche R (2021) Reliability optimization of power plant safety system using grey wolf optimizer and shuffled frog-leaping algorithm. In: Nature-inspired computing paradigms in systems. Academic Press, pp 1–13
Mythili S, Thiyagarajah K, Rajesh P, Shajin FH (2020) Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: an antlion optimiser and invasive weed optimisation algorithm. HKIE Trans 27(1):25–37
Nainan S, Kulkarni V (2020) Enhancement in speaker recognition for optimized speech features using GMM, SVM and 1-D CNN. Int J Speech Tech 24:809–822
Nassif AB, Shahin I, Hamsa S, Nemmour N, Hirose K (2021) CASA-based speaker identification using cascaded GMM-CNN classifier in noisy and emotional talking conditions. Appl Soft Comput 103:107141
Nicolini C, Forcellini G, Minati L, Bifone A (2020) Scale-resolved analysis of brain functional connectivity networks with spectral entropy. Neuroimage 211:116603
Rajesh P, Shajin F (2020) A multi-objective hybrid algorithm for planning electrical distribution system. Eur J Electr Eng 22(4–5):224–509
Ravanelli M, Bengio Y (2018) Speaker recognition from raw waveform with sincnet. In: 2018 IEEE Spoken Language Technology Workshop (SLT) IEEE, pp 1021–1028
Reddy V, Prakash G (2019) Enhanced key establishment technique for secure data access in cloud. In: 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) 1:1–4
Richard G, Virtanen T, Bello JP, Ono N, Glotin H (2017) Introduction to the special section on sound scene and event analysis. IEEE/ACM Trans Audio Speech Lang Process 25(6):1169–1171
Sangeetha J, Jayasankar T (2018) A novel whispered speaker identification system based on extreme learning machine. Int J Speech Technol 21(1):157–165
Shahin I, Nassif AB, Hamsa S (2020) Novel cascaded Gaussian mixture model-deep neural network classifier for speaker identification in emotional talking environments. Neural Comput Appl 32(7):2575–2587
Shajin FH, Rajesh P (2020) Trusted secure geographic routing protocol: outsider attack detection in mobile ad hoc networks by adopting trusted secure geographic routing protocol. Int J Pervasive Comput Commun. https://doi.org/10.1108/IJPCC-09-2020-0136
Shon S, Tang H, Glass J (2018) Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model. In: 2018 ieee spoken language technology workshop (slt). IEEE, pp 1007–1013
Sun L, Gu T, Xie K, Chen J (2019) Text-independent speaker identification based on deep Gaussian correlation supervector. Int J Speech Technol 22(2):449–457
Therese SS, Lingam C (2017) A linear visual assessment tendency based clustering with power normalized cepstral coefficients for audio signal recognition system. J Ambient Intell Humaniz Comput, pp 1–4
Thota MK, Shajin FH, Rajesh P (2020) Survey on software defect prediction techniques. Int J Appl Sci Eng 17(4):331–344
Venkatesan R, Ganesh AB (2017) Unsupervised auditory saliency enabled binaural scene analyzer for speaker localization and recognition. In: International symposium on signal processing and intelligent recognition systems. Springer, Cham, pp 337–350
Villalba J, Chen N, Snyder D, Garcia-Romero D, McCree A, Sell G, Borgstrom J, García-Perera LP, Richardson F, Dehak R, Torres-Carrasquillo PA (2020) State-of-the-art speaker recognition with neural network embeddings in NIST SRE18 and speakers in the wild evaluations. Comput Speech Lang 60:101026
Xu B, Wang W, Falzon G, Kwan P, Guo L, Sun Z, Li C (2020) Livestock classification and counting in quadcopter aerial images using Mask R-CNN. Int J Remote Sens 41(21):8121–8142
Zagagy B, Herman M, Levi O (2021) ACKEM: automatic classification, using KNN based ensemble modeling. In: Future of information and communication conference. Springer, Cham, pp 536–557
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Not applicable.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gaurav, Bhardwaj, S. & Agarwal, R. An efficient speaker identification framework based on Mask R-CNN classifier parameter optimized using hosted cuckoo optimization (HCO). J Ambient Intell Human Comput 14, 13613–13625 (2023). https://doi.org/10.1007/s12652-022-03828-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-022-03828-7