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(see image below)\n\n**Actually, cepstral distance is not a good measure for the speech recognition task because it ignores time warping. for more accuracy, you can use neural networks such as RNNs.**\n\n### notations\nquestion1:\n12 real cepstrum coefficients \u003cbr /\u003e\nquestion2: \n12 MFCC (Mel-frequency cepstral coefficients) \u003cbr /\u003e\nquestion3:\n12 MFCC + 1 Energy coefficient \u003cbr /\u003e \nquestion4:\n12 MFCC + 1 Energy coefficient and their first derivatives \u003cbr /\u003e\nquestion5:\n12 MFCC + 1 Energy coefficient and their first and second derivatives\n    \n### isolated digits or words recognition flowchart \n![1](https://user-images.githubusercontent.com/85555218/121799398-23c53780-cc41-11eb-8133-22f19fbfc7a8.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparham1998%2Fisolated-digits-recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fparham1998%2Fisolated-digits-recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparham1998%2Fisolated-digits-recognition/lists"}