1. A computer-implemented method for key phrase detection comprising: updating, at a current time instance, a start state based rejection model and a key phrase model associated with a predetermined key phrase based on scores of sub-phonetic units representative of received audio input, wherein the start state based rejection model includes a single rejection state having a plurality of rejection model self loops each associated with a particular score of the scores of sub-phonetic units, wherein the key phrase model includes a plurality of key phrase states interconnected by transitions therebetween, wherein the start state based rejection model and the key phrase model are connected by a first transition from the single rejection state to a first key phrase state of the plurality of key phrase states, and wherein said updating comprises: transitioning a score from a particular key phrase state of the plurality of key phrase states of the key phrase model to a next key phrase state of the plurality of key phrase states of the key phrase model; transitioning the score from the particular key phrase state to the single rejection state of the start state based rejection model; and generating a rejection likelihood score corresponding to the single rejection state of the start state based rejection model and a key phrase likelihood score corresponding to the key phrase model; and detecting the predetermined key phrase in the received audio input based on the rejection likelihood score and the key phrase likelihood score; and providing a wake indicator or a command in response to the detected predetermined key phrase.
3. The method of claim 1 , wherein said updating comprises: transitioning a second score from a second key phrase state of the plurality of key phrase states of the key phrase model to the single rejection state of the start state based rejection model; and determining a highest probability score from the particular scores of sub-phonetic units associated with the rejection model self loops of the start state based rejection model and adding the highest probability score to a maximum of the score transitioned from the particular key phrase state, the second score transitioned from the second key phrase state, and a previous score of the single rejection state to provide a score of the single rejection state at the current time instance.