Abstract
Various language identification tools and methods have been used in the real world. These applications can detect language using text or images. However, there is no speech-based language automated identification tool available. Therefore, many studies have been presented to overcome this problem. This work presents an automated high accurate language identification model and developed a new corpus for language identification. The developed language identification model uses two novel methods: (i) polymer pattern (PP) and (ii) tent maximum absolute pooling (TMAP). These methods help to extract both low- and high-frequency features. In order to choose the most informative features, a threshold-based iterative feature selector is presented. The proposed PP- and TMAP-based model has attained an accuracy of 97.87% and 99.70% using our newly developed and VoxForge datasets, respectively, with kNN classifier with tenfold cross-validation.
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References
Rosenthal S, Atanasova P, Karadzhov G, Zampieri M, Nakov P (2020) A large-scale semi-supervised dataset for offensive language identification. arXiv preprint
Takçi H, Ekinci E (2012) Minimal feature set in language identification and finding suitable classification method with it. Procedia Technol 1:444–448
Habic V, Semenov A, Pasiliao EL (2020) Multitask deep learning for native language identification. Knowl Based Syst 209:106440
Guha S, Das A, Singh PK, Ahmadian A, Senu N, Sarkar R (2020) Hybrid feature selection method based on harmony search and naked mole-rat algorithms for spoken language identification from audio signals. IEEE Access 8:182868–182887
Mukherjee H, Obaidullah SM, Santosh K, Phadikar S, Roy K (2020) A lazy learning-based language identification from speech using MFCC-2 features. Int J Mach Learn Cybern 11(1):1–14
Abdullah B, Avgustinova T, Möbius B, Klakow D (2020) Cross-domain adaptation of spoken language identification for related languages: the curious case of slavic languages. arXiv preprint
Shen P, Lu X, Li S, Kawai H (2020) Knowledge distillation-based representation learning for short-utterance spoken language identification. IEEE/ACM Trans Audio Speech Lang Process 28:2674–2683
Hughes B, Baldwin T, Bird S, Nicholson J, MacKinlay A (2006) Reconsidering language identification for written language resources
Li H, Ma B, Lee C-H (2006) A vector space modeling approach to spoken language identification. IEEE Trans Audio Speech Lang Process 15(1):271–284
Tong R, Ma B, Zhu D, Li H, Chng ES (2006) Integrating acoustic, prosodic and phonotactic features for spoken language identification. In: 2006 IEEE international conference on acoustics speech and signal processing proceedings. IEEE, pp I-I
Teixeira C, Trancoso I, Serralheiro A (1996) Accent identification. In: proceeding of fourth international conference on spoken language processing. ICSLP'96. IEEE, pp 1784–1787
Irtza S, Sethu V, Ambikairajah E, Li H (2018) Using language cluster models in hierarchical language identification. Speech Commun 100:30–40
Monteiro J, Alam J, Falk TH (2019) Residual convolutional neural network with attentive feature pooling for end-to-end language identification from short-duration speech. Comput Speech Lang 58:364–376
Xue J, Li B, Yan R, Gruen JR, Feng T, Joanisse MF, Malins JG (2020) The temporal dynamics of first and second language processing: ERPs to spoken words in Mandarin-English bilinguals. Neuropsychologia 146:107562
Poncelet J, Renkens V (2020) Low resource end-to-end spoken language understanding with capsule networks. Comput Speech Lang 66:101142
Deshwal D, Sangwan P, Kumar D (2020) A language identification system using hybrid features and back-propagation neural network. Appl Acoust 164:107289
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Raghu S, Sriraam N (2018) Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms. Expert Syst Appl 113:18–32
Montavon G (2009) Deep learning for spoken language identification. In: NIPS workshop on deep learning for speech recognition and related applications. Citeseer, pp 1–4
VoxForge (2020) VoxForge, free speech recognition, www.voxforge.org
. Lounnas K, Abbas M, Teffahi H, Lichouri M (2019) A language identification system based on voxforge speech corpus. In: international conference on advanced machine learning technologies and applications. Springer, pp 529-534
Kumar P, Biswas A, Mishra AN, Chandra M (2010) Spoken language identification using hybrid feature extraction methods. arXiv preprint
Cui H, Liu A, Zhang X, Chen X, Wang K, Chen X (2020) EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network. Knowl Based Syst 205:106243
Vuddagiri RK, Vydana HK, Vuppala AK (2018) Curriculum learning based approach for noise robust language identification using DNN with attention. Expert Syst Appl 110:290–297
Mounika K, Achanta S, Lakshmi H, Gangashetty SV, Vuppala AK (2016) An investigation of deep neural network architectures for language recognition in indian languages. In: INTERSPEECH. pp 2930–2933
Tang Z, Wang D, Chen Y, Chen Q (2017) AP17-OLR challenge: data, plan, and baseline. In: 2017 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC). IEEE, pp 749–753
Wang D, Li L, Tang D, Chen Q (2016) Ap16-ol7: A multilingual database for oriental languages and a language recognition baseline. In: 2016 Asia-Pacific signal and information processing association annual summit and conference (APSIPA). IEEE, pp 1–5
Dutta AK, Rao KS (2018) Language identification using phase information. Int J Speech Technol 21(3):509–519
Maity S, Vuppala AK, Rao KS, Nandi D (2012) IITKGP-MLILSC speech database for language identification. In: 2012 national conference on communications (NCC). IEEE, pp 1–5
Muthusamy YK, Cole RA, Oshika BT (1992) The OGI multi-language telephone speech corpus. In: second international conference on spoken language processing
Tang Z, Wang D, Song L (2019) AP19-OLR Challenge: three tasks and their baselines. In: 2019 Asia-pacific signal and information processing association annual summit and conference (APSIPA ASC), IEEE, pp 1917–1921
Revay S, Teschke M (2019) Multiclass language identification using deep learning on spectral images of audio signals. arXiv preprint
Bhanja CC, Laskar MA, Laskar RH (2019) A pre-classification-based language identification for Northeast Indian languages using prosody and spectral features. Circuits Syst Signal Process 38(5):2266–2296
Baba M, Imamura T, Hoshikawa N, Nakayama H, Ito T, Shiraki A (2020) Development of a multilingual digital signage system using a directional volumetric display and language identification. OSA Continuum 3(11):3187–3196
Blanchard D, Tetreault J, Higgins D, Cahill A, Chodorow M (2013) TOEFL11: A corpus of non‐native English. ETS Research Report Series 2013 (2):i-15
Granger S, Dagneaux E, Meunier F, Paquot M (2002) International corpus of learner english, (ICLE). Presses Universitaires de Louvain, Louvain-la-Neuve
Yasmin G, Das AK, Nayak J, Pelusi D, Ding W (2020) Graph based feature selection investigating boundary region of rough set for language identification. Expert Syst Appl 158:113575
Reddy VR, Maity S, Rao KS (2013) Identification of Indian languages using multi-level spectral and prosodic features. Int J Speech Technol 16(4):489–511
Sisodia DS, Nikhil S, Kiran GS, Sathvik P (2020) Ensemble learners for identification of spoken languages using mel frequency cepstral coefficients. In: 2nd international conference on data, engineering and applications (IDEA). IEEE, pp 1–5
Verma M, Buduru AB (2020) Fine-grained language identification with multilingual capsNet Model. In: 2020 IEEE sixth international conference on multimedia big data (BigMM), IEEE, pp 94–102
Hou W, Dong Y, Zhuang B, Yang L, Shi J, Shinozaki T (2020) Large-scale end-to-end multilingual speech recognition and language identification with multi-task learning. Babel 37(4k):10k
Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The World Wide Web Conference, pp 417–426
Bianchi FM, Grattarola D, Livi L, Alippi C (2021) Graph neural networks with convolutional arma filters. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2021.3054830
Levie R, Monti F, Bresson X, Bronstein MM (2018) Cayleynets: graph convolutional neural networks with complex rational spectral filters. IEEE Trans Signal Process 67(1):97–109
Such FP, Sah S, Dominguez MA, Pillai S, Zhang C, Michael A, Cahill ND, Ptucha R (2017) Robust spatial filtering with graph convolutional neural networks. IEEE J Sel Top Signal Process 11(6):884–896
Vries RD (2021) Perspective on AlphaFold 2 and advances in computational protein folding predictions.
Tuncer T (2021) A new stable nonlinear textural feature extraction method based EEG signal classification method using substitution Box of the Hamsi hash function: Hamsi pattern. Appl Acoust 172:107607
Tuncer T, Dogan S, Özyurt F, Belhaouari SB, Bensmail H (2020) Novel multi center and threshold ternary pattern based method for disease detection method using voice. IEEE Access 8:84532–84540
Maillo J, Ramírez S, Triguero I, Herrera F (2017) kNN-IS: an Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowl-Based Syst 117:3–15
Zhao W, Chellappa R, Nandhakumar N (1998) Empirical performance analysis of linear discriminant classifiers. In: Proceedings. 1998 IEEE computer society conference on computer vision and pattern recognition (Cat. No. 98CB36231), IEEE, pp 164–169
Vapnik V (1998) The support vector method of function estimation. In: Suykens JAK, Vandewalle J (eds) Nonlinear modeling. Springer, pp 55–85
Vapnik V (2013) The nature of statistical learning theory. Springer science & business media
Tuncer T, Dogan S, Pławiak P, Acharya UR (2019) Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowl Based Syst 186:104923
Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):6
Warrens MJ (2008) On the equivalence of Cohen’s kappa and the Hubert-Arabie adjusted Rand index. J Classif 25(2):177–183
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Tuncer, T., Dogan, S., Akbal, E. et al. Development of accurate automated language identification model using polymer pattern and tent maximum absolute pooling techniques. Neural Comput & Applic 34, 4875–4888 (2022). https://doi.org/10.1007/s00521-021-06678-0
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DOI: https://doi.org/10.1007/s00521-021-06678-0