A computer implemented method of reducing false alarms in a speech recognition system, wherein the system comprises a module for determining false alarms from a keyword spotter which utilizes input from knowledge sources and a front end feature calculator, the front end feature calculator being for extracting feature vectors from an input audio signal, the method comprising the steps of: a. determining, by the module, a context of a set of words, wherein said words are provided to the module by the keyword spotter and feature vectors are provided to the module by the front end feature calculator and defining, by the module, a size of a local external context and a size of a local internal context of said set of words, wherein the local external context is a local acoustical external context referring to an acoustical context of the word neighboring the beginning and the end of a word lying outside the boundaries of the word and the local internal context is a local acoustical internal context referring to an acoustical context of the word neighboring the beginning and the end of a word contained within the boundaries of the word, wherein the acoustical context refers to a sequence of the feature vectors; b. obtaining a set of training examples for said set of words from the knowledge sources by the module, wherein said knowledge sources comprise at least an acoustic model and a pronunciation dictionary; c. generating, by the module, a set of models, from the training examples, of the determined contexts, wherein the generation of the set of models includes: a compact representation of the left internal context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the right internal context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the left external context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the right external context in the form of spectral, cepstral, or sinusoidal descriptions, d. comparing, by the module, the set of words with said set of models, wherein each word of the set of words is compared with the models for the left internal context, the right internal context, the left external context, and the right external context; e. obtaining thresholds for model comparison; f. determining, by the module, if a result of comparing said set of words with the set of models is within thresholds; and g. rejecting, by the module, a word of the set of words if a result of the comparison with one model of the set of models is not within the threshold. a. determining, by the module, a context of a set of words, wherein said words are provided to the module by the keyword spotter and feature vectors are provided to the module by the front end feature calculator and defining, by the module, a size of a local external context and a size of a local internal context of said set of words, wherein the local external context is a local acoustical external context referring to an acoustical context of the word neighboring the beginning and the end of a word lying outside the boundaries of the word and the local internal context is a local acoustical internal context referring to an acoustical context of the word neighboring the beginning and the end of a word contained within the boundaries of the word, wherein the acoustical context refers to a sequence of the feature vectors; b. obtaining a set of training examples for said set of words from the knowledge sources by the module, wherein said knowledge sources comprise at least an acoustic model and a pronunciation dictionary; c. generating, by the module, a set of models, from the training examples, of the determined contexts, wherein the generation of the set of models includes: a compact representation of the left internal context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the right internal context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the left external context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the right external context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the left internal context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the right internal context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the left external context in the form of spectral, cepstral, or sinusoidal descriptions, a compact representation of the right external context in the form of spectral, cepstral, or sinusoidal descriptions, d. comparing, by the module, the set of words with said set of models, wherein each word of the set of words is compared with the models for the left internal context, the right internal context, the left external context, and the right external context; e. obtaining thresholds for model comparison; f. determining, by the module, if a result of comparing said set of words with the set of models is within thresholds; and g. rejecting, by the module, a word of the set of words if a result of the comparison with one model of the set of models is not within the threshold.