Speech recognition method and apparatus, and computer-readable storage medium

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,
• wherein the determining first speech features of the to-be-trained audios with the maximum sampling rate comprises performing global normalization processing on the first Mel-spectrum features to obtain the first speech features and the global normalization processing comprises:
• acquiring second amplitude values corresponding to each Mel-filter serial number in the first Mel-spectrum features;
• calculating a mean and a standard deviation of all second amplitude values as a global mean and a global standard deviation;
• calculating a second difference between each of the second amplitude values and the global mean thereof;
• calculating a ratio of each second difference to the global standard deviation as a normalized second amplitude value corresponding to each second amplitude value;
• and
• replacing each second amplitude value in the first Mel-spectrum features with the normalized second amplitude value corresponding to each second amplitude value.

A non-transitory computer-storable medium having stored thereon computer program instructions which, when executed by a processor, implement a speech recognition method, which comprises:

• 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,
• wherein the determining second speech features of the to-be-trained audios with the other sampling rate comprises performing local normalization processing on the second Mel-spectrum features to obtain the second speech features and the local normalization processing comprises:
• according to a maximum linear-spectrum frequency corresponding to the to-be-trained audios with the other sampling rate, acquiring a Mel-spectrum frequency corresponding to the maximum linear-spectrum frequency;
• calculating a maximum Mel-filter serial number corresponding to the Mel-spectrum frequency;
• acquiring first amplitude values corresponding to each other Mel-filter serial number in the second Mel-spectrum features, the other Mel-filter serial number being a Mel-filter serial number less than or equal to the maximum Mel-filter serial number;
• respectively calculating a mean and a standard deviation of all first amplitude values as a local mean and a local standard deviation;
• calculating a first difference between each of the first amplitude values and the local mean thereof;
• calculating a ratio of each first difference to the local standard deviation as a normalized first amplitude value corresponding to each first amplitude value;
• and
• replacing each first amplitude value in the second Mel-spectrum features with the normalized first amplitude value corresponding to each first amplitude value.