1. A method for improving a phrase spotting confidence score, comprising: providing, to a processor, a test speech and a transcription thereof; obtaining, by the processor, an input phrase in a textual form for spotting in the provided test speech; partitioning, by the processor, the input phrase into one or more phoneme sequences; searching the one or more phoneme sequences in a phoneme lattice, by the processor, to find candidate events of the input phrase in the test speech; calculating, by the processor, a posterior probability estimation (PPE) score for each of the candidate events; generating, by the processor, a phonetic transcription for the input phrase; applying, by the processor, a classifying model to the phonetic transcription of the test speech according to a posterior probability feature based on the calculated PPE scores and to a set of phrase spotting features related to the input phrase, the phrase spotting features selected from features extracted from a decoding process of the phrase, context-based features, or a combination thereof, thereby spotting the given phrase with a confidence score; and using a trained monotone transformation, by the processor, to simplify the feature normalization and a Support Vector Machine (SVM) model into a single vector in a size of the phrase spotting feature set; wherein the classifying model utilizes the Support Vector Machine (SVM) with a linear kernel; wherein the features related to the input phrase are selected from phrase-related features, event-related features, decoding process features, context-based features, or a combination thereof; wherein at least one feature related to the input phrase is based on a number of long phonemes of the input phrase with phoneme time durations longer than a predefined threshold; and wherein the classifying model is priorly trained for spotting the input phrase according to the posterior probability feature and to the set of phrase spotting features.
1. A method for improving a phrase spotting confidence score, comprising: providing, to a processor, a test speech and a transcription thereof; obtaining, by the processor, an input phrase in a textual form for spotting in the provided test speech; partitioning, by the processor, the input phrase into one or more phoneme sequences; searching the one or more phoneme sequences in a phoneme lattice, by the processor, to find candidate events of the input phrase in the test speech; calculating, by the processor, a posterior probability estimation (PPE) score for each of the candidate events; generating, by the processor, a phonetic transcription for the input phrase; applying, by the processor, a classifying model to the phonetic transcription of the test speech according to a posterior probability feature based on the calculated PPE scores and to a set of phrase spotting features related to the input phrase, the phrase spotting features selected from features extracted from a decoding process of the phrase, context-based features, or a combination thereof, thereby spotting the given phrase with a confidence score; and using a trained monotone transformation, by the processor, to simplify the feature normalization and a Support Vector Machine (SVM) model into a single vector in a size of the phrase spotting feature set; wherein the classifying model utilizes the Support Vector Machine (SVM) with a linear kernel; wherein the features related to the input phrase are selected from phrase-related features, event-related features, decoding process features, context-based features, or a combination thereof; wherein at least one feature related to the input phrase is based on a number of long phonemes of the input phrase with phoneme time durations longer than a predefined threshold; and wherein the classifying model is priorly trained for spotting the input phrase according to the posterior probability feature and to the set of phrase spotting features. 5. The method according to claim 1 , wherein the training of the classifying model includes adaptation of the classifying model to a specific lexicon.