endobj << /Pg 131 0 R /K [ 1267 1268 1269 1270 ] >> /P 2054 0 R endobj /P 818 0 R >> endobj /Pg 129 0 R endobj /S /Span >> /S /P /Pg 29 0 R /K [ 1086 1087 1088 1089 ] /S /Span /S /P /S /Span /P 1400 0 R << /Pg 129 0 R /P 1703 0 R /S /P 2005 0 obj 592 0 obj /S /TD endobj << >> endobj 2066 0 obj /Pg 129 0 R 2872 0 obj /S /Span << >> /P 2625 0 R 2110 0 obj endobj << 725 0 R 726 0 R 726 0 R 726 0 R 728 0 R 729 0 R 729 0 R 729 0 R 731 0 R 732 0 R 732 0 R 2440 0 obj endobj /S /TD /Pg 131 0 R /Pg 129 0 R /P 2060 0 R /S /P /Pg 3 0 R PLP and RASTA (and MFCC, and inversion) in Matlab using ... /K [ 2748 0 R 2749 0 R ] /P 2240 0 R /Pg 131 0 R 775 0 obj 1919 0 obj /K [ 2949 0 R ] Also, a comparative analysis of cepstum, Mel-frequency Cepstral Coefficients (MFCC) and synthetically enlarged … << 1846 0 obj /Pg 458 0 R >> /P 2177 0 R /S /Span 2657 0 obj /P 1952 0 R /Pg 129 0 R >> >> << /K [ 748 749 750 ] /Pg 129 0 R endobj /P 1813 0 R /S /TR /S /P /Pg 129 0 R /S /Span /K 1319 >> >> >> << >> endobj /Pg 129 0 R /K [ 691 0 R ] << << /Pg 129 0 R /S /Span >> endobj DCNN (Deep Convolutional Neural Networks), neuron in the layer to every neuron the following layer. /K 1030 >> endobj 2556 0 obj /Pg 129 0 R /K [ 578 579 580 581 ] >> endobj /K 1261 >> /S /Span /K 163 /S /Span /K [ 2696 0 R 2697 0 R ] /P 1806 0 R /K 561 /P 1353 0 R /P 721 0 R >> /P 2477 0 R >> << << >> >> /P 2529 0 R endobj >> >> /Pg 131 0 R << >> << /Pg 129 0 R >> 2476 0 obj 825 0 obj /K [ 2459 0 R 2460 0 R ] /P 2385 0 R >> /S /P endobj /K [ 1401 1402 1403 1404 ] The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. >> >> endobj >> >> << >> /P 2811 0 R /Pg 129 0 R /Pg 129 0 R 3038 0 obj /K [ 735 736 737 738 ] /P 470 0 R ResearchGate has not been able to resolve any citations for this publication. << endobj /K [ 2858 0 R 2859 0 R ] /Pg 131 0 R /P 470 0 R >> << 2762 0 obj /P 903 0 R endobj /K [ 2465 0 R 2466 0 R ] << /Pg 122 0 R /P 1240 0 R /Pg 129 0 R /S /Span endobj /P 2868 0 R /K [ 1368 1369 1370 1371 ] endobj endobj /Pg 131 0 R >> >> /Pg 131 0 R /P 2670 0 R >> endobj endobj << /P 470 0 R 2628 0 obj /S /TD recognizing who said it, as opposed to what was said), I'm thinking that perhaps a different windowing function is used, or maybe a different type of filter bank is used to transform the spectrum to the mel scale, but I'm not entirely sure--perhaps it is a different process altogether. /P 1589 0 R /Pg 131 0 R /P 1136 0 R /Pg 129 0 R >> /K [ 25 ] /S /P /P 2240 0 R << /P 2798 0 R /S /Textbox >> >> /K 45 /K [ 4 ] /Pg 131 0 R /P 1080 0 R Performance Analysis of MFCC And LPCC Techniques In ... /P 1108 0 R /P 2538 0 R << /P 1542 0 R 547 0 obj >> /Pg 458 0 R /K [ 2690 0 R 2691 0 R ] endobj endobj Follow; Download. << /S /P /P 1691 0 R >> >> >> /Pg 129 0 R /S /P >> endobj /S /TD /Pg 129 0 R /P 2240 0 R /S /TD endobj 2377 0 R 2380 0 R ] /K 1960 /Pg 129 0 R << 2278 0 obj 2540 0 obj << << 2908 0 obj >> endobj 897 0 obj << where N is the window length. /K [ 2953 0 R ] endobj 1171 0 obj /P 2794 0 R /Pg 131 0 R /P 1735 0 R << /Pg 131 0 R /S /P /P 1039 0 R /S /P << << << /S /Span /S /Span /P 1394 0 R 1722 0 obj 2901 0 obj /S /P >> 2218 0 obj /P 2587 0 R >> << /S /P endobj /P 931 0 R /S /Span endobj /P 724 0 R << >> 2849 0 obj Speech Recognition using Digital Signal Processing Mr. Maruti Saundade Mr.Pandurang Kurle Abstract: -Speech recognition methods can be divided into text-independent and text dependent methods. /S /TD << /K [ 95 ] /S /P /Pg 129 0 R << << /K [ 239 240 241 242 ] 1704 0 obj /K [ 229 230 231 232 ] /Pg 129 0 R << /P 470 0 R 2371 0 obj /P 2035 0 R endobj /K [ 61 ] /K [ 115 116 117 ] /S /TD 750 0 R 750 0 R 750 0 R 752 0 R 753 0 R 753 0 R 753 0 R 754 0 R 757 0 R 758 0 R 758 0 R /K [ 2401 0 R 2402 0 R ] << /K [ 192 193 194 ] /Pg 131 0 R >> /P 470 0 R 2727 0 obj /Pg 131 0 R /P 1712 0 R >> 2605 0 obj << /Pg 107 0 R << Extracting Sub-glottal and Supra-glottal Features from ... << >> /S /Span << << /Pg 129 0 R << /K 649 /K [ 1722 0 R 1723 0 R ] << /Pg 129 0 R /S /P /P 1321 0 R /K [ 2614 0 R 2615 0 R ] /K [ 2703 0 R 2706 0 R 2709 0 R 2712 0 R 2715 0 R 2718 0 R 2721 0 R 2724 0 R 2727 0 R 768 0 obj >> /K [ 31 ] /S /TD 1298 0 obj endobj /Pg 458 0 R << /K 25 /K [ 8 ] >> >> /S /H2 /S /P /Pg 129 0 R >> /P 1416 0 R /S /TD /S /P endobj endobj endobj /Pg 129 0 R /K 2168 /S /Span 2084 0 obj >> /P 2110 0 R /P 1460 0 R 2708 0 obj /S /Span /Pg 129 0 R /S /Span /K 2127 /Pg 131 0 R endobj /P 1958 0 R /K [ 178 179 180 181 ] << >> >> /Pg 458 0 R << /Pg 131 0 R << /S /P /K [ 61 ] /S /TD /S /Span /S /Span >> /P 2631 0 R >> >> /S /TD endobj /Pg 463 0 R /S /TD /P 759 0 R << /S /P << /K [ 1366 0 R 1367 0 R ] /K 1099 >> << /Pg 131 0 R endobj /S /TD 1619 0 obj endobj /Pg 129 0 R /K [ 541 542 543 544 ] /K [ 1689 0 R 1690 0 R ] /K 592 >> /S /Span /P 470 0 R endobj /Pg 131 0 R << >> 1996 0 obj endobj /S /TD /K 36 /K [ 1733 0 R 1734 0 R ] /S /P /Pg 458 0 R endobj /P 2851 0 R /S /P /S /TD /Pg 129 0 R /Pg 129 0 R /K [ 760 0 R 761 0 R ] /K [ 752 753 754 755 ] /S /P /K [ 588 589 590 591 ] /Pg 29 0 R >> /K [ 1915 1916 1917 1918 ] /P 2240 0 R /S /TD /Pg 129 0 R << << endobj << << /K 724 >> /P 470 0 R >> /Pg 129 0 R /Pg 458 0 R /Pg 129 0 R endobj << 636 0 obj endobj /P 1994 0 R /P 2830 0 R /K [ 53 54 ] endobj 896 0 obj /P 806 0 R 1409 0 obj /S /TD audio - voice recognition vs speech recognition MFCC ... 633 0 R 634 0 R 635 0 R 636 0 R 637 0 R 638 0 R 639 0 R 640 0 R 641 0 R 642 0 R 643 0 R /S /LI /Pg 129 0 R /S /P /S /P /Pg 129 0 R >> >> << /S /P /S /P /K [ 1730 0 R 1731 0 R ] One solution to this problem (vocal tract length normalization) is to find a linear transform of the MFCC vector such that, when applied to Alice's MFCC data, make it similar to what the model has captured. /Pg 131 0 R << /K [ 1590 1591 1592 ] >> /S /TD /K [ 1892 0 R 1893 0 R ] >> /S /P >> /S /Span << /K 135 << /K [ 1134 0 R 1135 0 R ] /S /TD endobj 1532 0 R 1532 0 R 1532 0 R 1534 0 R 1535 0 R 1535 0 R 1535 0 R 1535 0 R 1537 0 R /S /TD /Pg 129 0 R >> << MFCC AND DTW BASED SPEECH RECOGNITION /S /TD 992 0 obj /Pg 29 0 R endobj 1337 0 obj /Pg 129 0 R /K [ 1289 0 R 1290 0 R ] Keywords-- Automatic Speech Recognition, Mel frequency Cepstral Coefficient, Predictive Linear Coding . 2850 0 R 2852 0 R 2853 0 R 2853 0 R 2855 0 R 2856 0 R 2856 0 R 2858 0 R 2859 0 R >> /S /Span 1451 0 obj /P 2348 0 R endobj /P 1505 0 R /S /TD In this research work, the authors estimate number of Gaussian mixture component for Hindi database based upon the size of vocabulary.Mel frequency cepstral feature and perceptual linear predictive feature along with its extended variations with delta-delta-delta feature have been used to evaluate this number based on optimal recognition score of the system . 1453 0 obj endobj << >> /K [ 2163 0 R 2164 0 R ] /S /P endobj /K [ 470 471 472 ] 1007 0 obj endobj /Pg 131 0 R >> /Pg 131 0 R << endobj /K 29 /P 799 0 R /P 2619 0 R /S /P /P 2734 0 R 2942 0 obj /K [ 1421 0 R 1422 0 R ] /Pg 131 0 R endobj 1078 0 obj << >> /S /TD /QuickPDFFb3b6ace8 37 0 R endobj >> 2509 0 R 2512 0 R 2513 0 R 2513 0 R 2513 0 R 2513 0 R 2515 0 R 2516 0 R 2516 0 R >> 874 0 obj /S /P endobj /Pg 129 0 R endobj 1591 0 obj endobj /K [ 1 ] /P 2435 0 R /Pg 131 0 R 1235 0 R 1237 0 R 1238 0 R 1238 0 R 1238 0 R 1238 0 R 1239 0 R 1242 0 R 1243 0 R /Pg 129 0 R endobj /P 2349 0 R /P 1769 0 R /K [ 763 0 R 764 0 R ] << /P 887 0 R << >> /Pg 129 0 R 871 0 obj >> endobj >> /Pg 129 0 R /Pg 107 0 R /S /Span 1177 0 obj /Pg 131 0 R 1067 0 obj /Pg 129 0 R /Pg 131 0 R << /Pg 129 0 R /P 1365 0 R /P 843 0 R endobj endobj /Pg 129 0 R /S /P 1079 0 obj endobj /K [ 568 569 570 571 ] /K 2224 >> endobj 2305 0 obj << >> endobj endobj 1784 0 obj >> 952 0 obj 1672 0 obj /K 413 /S /P /P 496 0 R 2164 0 obj >> Speech detection using Mel-Frequency(MFCC) in R Studio ...

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