This paper describes a set of modeling techniques for detecting a small vocabulary of keywords in running conversational speech. The techniques are applied in the context of a hidden Markov model (HMM) based continuous speech recognition (CSR) approach to keyword spotting. The word spotting task is derived from the Switchboard conversational speech corpus, and involves unconstrained conversational speech utterances spoken over the public switched telephone network. The utterances in this task contain many of the artifacts that are characteristic of unconstrained speech as it appears in many telecommunications based automatic speech recognition (ASR) applications. Results are presented for an experimental study that was performed on this task. Performance was measured by computing the percentage correct keyword detection over a range of false alarm rates evaluated over 2โข2 h of speech for a 20 keyword vocabulary. The results of the study demonstrate the importance of several techniques. These techniques include the use of decision tree based allophone clustering for defining acoustic subword units, different representations for non-vocabulary words appearing in the input utterance, and the definition of simple language models for keyword detection. Decision tree based allophone clustering resulted in a significant increase in keyword detection performance over that obtained using tri-phone based subword units while at the same time reducing the size of the inventory of subword acoustic models by 40%. More complex representations of non-vocabulary speech were also found to significantly improve keyword detection performance; however, these representations also resulted in a significant increase in computational complexity.