Classification of audio as speech or non-speech using multiple threshold values

A computer system comprising:

• a processor;
• a memory coupled to the processor, the memory storing instructions that cause the processor to:
• separate at least a portion of an audio signal into a plurality of frames;
• extract line spectrum pairs from each of the plurality of frames;
• and
• use at least the line spectrum pairs to classify at least the portion as either speech or non-speech, wherein to use at least the line spectrum pairs is to:
• generate an input Gaussian Model corresponding to the plurality of frames based on the extracted line spectrum pairs;
• identify one of a plurality of trained Gaussian Models that is closest to the input Gaussian Model;
• determine a distance between the input Gaussian Model and the closest trained Gaussian Model;
• and
• classify at least the portion as non-speech if the distance is greater than a first threshold value;
• determine an energy distribution of the plurality of frames in a first bandwidth;
• and
• classify at least the portion as non-speech if the distance is greater than a second threshold value and the energy distribution of the plurality of frames in the first bandwidth is less than a third threshold value, wherein the second threshold value is less than the first threshold value, whereby an output facilitates the classification of the portion as non-speech.

A computer system to classify audio as either speech or non-speech, the computer system comprising:

• means for separating at least a portion of an audio signal representing input audio into a plurality of frames;
• means for extracting line spectrum pairs from each of the plurality of frames;
• and
• means for using at least the line spectrum pairs to classify at least the portion as either speech or non-speech, whereby an output facilitates the classification of the portion as either speech or non-speech, wherein the means for using comprises:
• means for generating an input Gaussian Model corresponding to the plurality of frames based on the extracted line spectrum pairs;
• means for identifying one of a plurality of trained Gaussian Models that is closest to the input Gaussian Model;
• means for determining a distance between the input Gaussian Model and the closest trained Gaussian Model;
• and
• means for classifying at least the portion as non-speech if the distance is greater than a first threshold value;
• means for determining an energy distribution of the plurality of frames in a first bandwidth;
• and
• means for classifying at least the portion as non-speech if the distance is greater than a second threshold value and the energy distribution of the plurality of frames in the first bandwidth is less than a third threshold value, wherein the second threshold value is less than the first threshold value.