Keyword spotting is a very forward-looking and promising branch of speech recognition. This paper presents a HMM-based keyword spotting system, which works with a new algorithm.The first discussion topic is the description of the search algorithm, that needs no representation of the non-keyword parts of the speech signal. For this purpose, the computation of the HMM scores and the Viterbi algorithm had to be modified. The keyword HMMs are not concatenated with other HMMs, so that there is no necessity for filler or garbage models. As a further advantage, this algorithm needs only low computional expense and storage requirement.The second discussion topic is the determination of a optimal decision threshold for each keyword. In order two decide between the two possibilities "keyword was spoken" and "keyword was not spoken", the scores of the keywords are compared with keyword specific decision thresholds. This paper introduces a method to fix decision thresholds in advance. Starting with measured phoneme distributions, the score distributions of whole keyword models can be calculated. Furthermore, these keyword distributions form the basis of the computation of decision thresholds.Tests with spontaneous speech databases yielded 73.9% Figure-OfMerit when using context-dependent HMMs. The detection rate at 10 fa/kw/h comes to 80%.

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