A speech recognition method, comprising: acquiring first linear spectrums corresponding to to-be-trained audios with different sampling rates, wherein an abscissa of the first linear spectrums is a spectrum-sequence serial number, an ordinate of the first linear spectrums is a frequency-domain serial number, and a value of a coordinate point determined by the abscissa and the ordinate is an original amplitude value corresponding to the to-be-trained audios; determining a maximum sampling rate and other sampling rate than the maximum sampling rate in the different sampling rates; determining a maximum frequency-domain serial number of the first linear spectrums corresponding to the other sampling rate as a first frequency-domain serial number; determining a maximum frequency-domain serial number of the first linear spectrums corresponding to the maximum sampling rate as a second frequency-domain serial number; setting, to zero, amplitude values corresponding to each frequency-domain serial number that is greater than the first frequency-domain serial number and less than or equal to the second frequency-domain serial number, in the first linear spectrums corresponding to the other sampling rate, to obtain second linear spectrums corresponding to the other sampling rate; determining first speech features of the to-be-trained audios with the maximum sampling rate according to first Mel-spectrum features of the first linear spectrums corresponding to the maximum sampling rate; determining second speech features of the to-be-trained audios with the other sampling rate according to second Mel-spectrum features of the second linear spectrums corresponding to the other sampling rate; and training a machine learning model by using the first speech features and the second speech features. acquiring first linear spectrums corresponding to to-be-trained audios with different sampling rates, wherein an abscissa of the first linear spectrums is a spectrum-sequence serial number, an ordinate of the first linear spectrums is a frequency-domain serial number, and a value of a coordinate point determined by the abscissa and the ordinate is an original amplitude value corresponding to the to-be-trained audios; determining a maximum sampling rate and other sampling rate than the maximum sampling rate in the different sampling rates; determining a maximum frequency-domain serial number of the first linear spectrums corresponding to the other sampling rate as a first frequency-domain serial number; determining a maximum frequency-domain serial number of the first linear spectrums corresponding to the maximum sampling rate as a second frequency-domain serial number; setting, to zero, amplitude values corresponding to each frequency-domain serial number that is greater than the first frequency-domain serial number and less than or equal to the second frequency-domain serial number, in the first linear spectrums corresponding to the other sampling rate, to obtain second linear spectrums corresponding to the other sampling rate; determining first speech features of the to-be-trained audios with the maximum sampling rate according to first Mel-spectrum features of the first linear spectrums corresponding to the maximum sampling rate; determining second speech features of the to-be-trained audios with the other sampling rate according to second Mel-spectrum features of the second linear spectrums corresponding to the other sampling rate; and training a machine learning model by using the first speech features and the second speech features.