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Keyword spotting (KWS) refers to detection of a limited number of given keywords in speech utterances. In this paper, we evaluate a robust keyword spotting system based on hidden markov models for speaker independent Persian conversational telephone speech. Performance of base line keyword spotter is improved by means of normalizing features using cepstral mean and variance normalization (CMVN) and cepstral gain normalization (CGN). And better performance is gained by applying auto-regressive moving average (ARMA) filter on normalized features. Experimental results show that although all these methods improve keyword spotting performance, CMVN and ARMA (MVA) processing of PLP features works much better on our Persian conversational telephone speech database and 41% improvement to baseline system is achieved at false alarm (FA) rate equal to 8.6 FA/KW/Hour.

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