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.