A device comprising: memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table, the LUT indicating a predicted hidden vector to be provided in response to a phoneme of a user-specified wake word, wherein the predicted hidden vector is a pre-computed vector computed from a last predicted label and stored in the LUT; a microphone to capture audio; a feature extractor configured to: receive the audio from the microphone and produce features of a frame of the audio; an encoder configured to receive the features and provide a second hidden vector; wherein the encoder acts as acoustic model and the predicted hidden vector acts as language model; a joint network configured to: join the predicted hidden vector and the second hidden vector and to output a posterior of a predicted label from the LUT and the second hidden vector; processing circuitry configured to: determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word; and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word. memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table, the LUT indicating a predicted hidden vector to be provided in response to a phoneme of a user-specified wake word, wherein the predicted hidden vector is a pre-computed vector computed from a last predicted label and stored in the LUT; a microphone to capture audio; a feature extractor configured to: receive the audio from the microphone and produce features of a frame of the audio; an encoder configured to receive the features and provide a second hidden vector; wherein the encoder acts as acoustic model and the predicted hidden vector acts as language model; a joint network configured to: join the predicted hidden vector and the second hidden vector and to output a posterior of a predicted label from the LUT and the second hidden vector; processing circuitry configured to: determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word; and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word. receive the audio from the microphone and produce features of a frame of the audio; an encoder configured to receive the features and provide a second hidden vector; wherein the encoder acts as acoustic model and the predicted hidden vector acts as language model; a joint network configured to: join the predicted hidden vector and the second hidden vector and to output a posterior of a predicted label from the LUT and the second hidden vector; processing circuitry configured to: determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word; and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word. join the predicted hidden vector and the second hidden vector and to output a posterior of a predicted label from the LUT and the second hidden vector; processing circuitry configured to: determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word; and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word. determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word; and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.