Abstract: Technologies for identifying sounds are disclosed. A sound identification device may capture sound data, and split the sound data into frames. The sound identification device may then determine an acoustic feature vector for each frame, and determine parameters based on how each acoustic feature varies over the duration of time corresponding to the frames. The sound identification device may then determine if the sound matches a pre-defined sound based on the parameters. In one embodiment, the sound identification device may be a baby monitor, and the pre-defined sound may be a baby crying.

Abstract: Techniques related to key phrase detection for applications such as wake on voice are discussed. Such techniques may include determining a sequence of audio units for received audio input representing a user defined key phrase, eliminating audio units from the sequence to generate a final sequence of audio units, and generating a key phrase recognition model representing the user defined key phrase based on the final sequence.

Abstract: Techniques related to key phrase detection for applications such as wake on voice are discussed. Such techniques may include updating a start state based rejection model and a key phrase model based on scores of sub-phonetic units from an acoustic model to generate a rejection likelihood score and a key phrase likelihood score and determining whether received audio input is associated with a predetermined key phrase based on the rejection likelihood score and the key phrase likelihood score.

Abstract: Techniques related to key phrase detection for applications such as wake on voice are discussed. Such techniques may include intermediate scoring of a state or states of a key phrase model and/or a backward transition or rejection loopback from a state of the key phrase model to a rejection model to reduce false accepts based on received utterances.

Abstract: Techniques related to detecting replay attacks on automatic speaker verification systems are discussed. Such techniques may include receiving an utterance from a user or a device playing back the utterance, determining features associated with the utterance, and classifying the utterance in a replay utterance class or an original utterance class based on a statistical classification or a margin classification of the utterance using the features.

Abstract: Technologies for identifying sounds are disclosed. A sound identification device may capture sound data, and split the sound data into frames. The sound identification device may then determine an acoustic feature vector for each frame, and determine parameters based on how each acoustic feature varies over the duration of time corresponding to the frames. The sound identification device may then determine if the sound matches a pre-defined sound based on the parameters. In one embodiment, the sound identification device may be a baby monitor, and the pre-defined sound may be a baby crying.

Abstract: Techniques related to key phrase detection for applications such as wake on voice are discussed. Such techniques may include updating a start state based rejection model and a key phrase model based on scores of sub-phonetic units from an acoustic model to generate a rejection likelihood score and a key phrase likelihood score and determining whether received audio input is associated with a predetermined key phrase based on the rejection likelihood score and the key phrase likelihood score.

Abstract: Various systems, apparatuses, and methods for training classifiers using selected cohort sample subsets are disclosed herein, in an example, a set of target supervectors, representing a target class, is received, and a set of cohort supervectors, representing a cohort class, is received. A distance metric is calculated from a respective cohort supervector to a respective target supervector, and a proper subset of cohort supervectors are selected based on the calculated distance metrics. The set of target supervectors and the selected proper subset of cohort supervectors are used to train a classifier. Further examples described herein describe how training classifiers using selected cohort sample subsets may be used to increase performance and decrease resource consumption in voice biometric systems.