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Cited by 36 publications
(13 citation statements)
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“…MFCCs are widely successful in the recognition of various types of audio signals as well as in various human speech processing tasks and are standard in such studies. Some examples include the use of MFCC-based features for language identification systems [ 50 ], speech emotion recognition [ 53 ], and speaker identification [ 65 ]. In [ 50 ], a comprehensive review on the use of MFCC-based features for language identification is presented.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
See 1 more Smart Citation
“…MFCCs are widely successful in the recognition of various types of audio signals as well as in various human speech processing tasks and are standard in such studies. Some examples include the use of MFCC-based features for language identification systems [ 50 ], speech emotion recognition [ 53 ], and speaker identification [ 65 ]. In [ 50 ], a comprehensive review on the use of MFCC-based features for language identification is presented.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…Some examples include the use of MFCC-based features for language identification systems [ 50 ], speech emotion recognition [ 53 ], and speaker identification [ 65 ]. In [ 50 ], a comprehensive review on the use of MFCC-based features for language identification is presented. Authors further propose a second-level MFCC-based feature (MFCC-2) that handles the large and uneven dimensionality of MFCC.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…In this paper, the work is presented in three main subsections namely Pre-processing (IV-A), Feature Extraction Cimarusti et al [12] Polynomial classification using LPC features 8 84% Jerry T. Foil [10] Formant and prosodic feature-based language identification 3 64% Marc A. Zissman [13] HMM based language identification 20 92% M. Sugiyama [14] Vector Quantization Technique based language identification 20 80% Gazeau et al [15] HMM based language identification 4 70% Revay et al [16] log-Mel spectra based DNN approach for language identification 6 89% Bartz et al [18] CRNN based language identification 6 91% Mukherjee et al [20] MFCC-2 based language identification 3 98.09%…”
Section: Methodsmentioning
confidence: 99%
“…The audio files were obtained from VoxForge dataset and the accuracy achieved was 95.4%. Mukherjee et al [20] proposed a new second level Mel frequency cepstral coefficient (MFCC-2) based features to overcome the large and uneven dimensionality of MFCC [21]. This method was used to identify 3 languages namely English, Bangla and Hindi and the dataset was prepared using 12,000 utterances of numerals and 41,884 clips extracted from YouTube videos.…”
Section: Related Workmentioning
confidence: 99%
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