This paper presents a newly formulated speech, recognition algorithm for keyword spotting which uses a feature enhancing artificial neural network, a semi-continuous hidden Markov model, and a likelihood ratio test based on optimal detection theory to make decisions regarding possible keywords. The speech recognizer can be used to detect the occurrences of a single word within connected input speech streams in noisefree neutral or Lombard stressed environments. A keyword-dependent neural network [1] enhances speech, parameters and reduces the probability of false acceptances of non-keywords by adapting its weights and input layer width based on extracted speech characteristics [2]. Using the neural network reduces false acceptances by more than for mono-syllable keywords in a defined keyword spotting application [3]. Enhanced features are submitted to a semi-continuous hidden Markov model which produces a score indicating the presence of the represented keyword. A likelihood ratio test uses functions formed from keyword and non-keyword recognizer training data for detection. Receiver operating characteristics (ROC's) show that the new recognition algorithm can improve keyword spotting performance for neutral and Lombard effect speaking conditions.