Abstract: Detecting an end-point of user's voice command or utterance with high accuracy is critical in automatic speech recognition (ASR)-based human machine interface. If an ASR system incorrectly detects an end-point of utterance and transmits this incomplete sentence to other processing blocks for further processing, it is likely the processed result would lead to incorrect interpretation. A method includes selecting a first semantic network based on context of the audio signal and more accurately detecting the end-point of user's utterance included in the audio signal based on the first semantic network and also based on at least one timeout threshold associated with the first semantic network.

Abstract: Detecting an end-point of user's voice command or utterance with high accuracy is critical in automatic speech recognition (ASR)-based human machine interface. If an ASR system incorrectly detects an end-point of utterance and transmits this incomplete sentence to other processing blocks for further processing, it is likely the processed result would lead to incorrect interpretation. A method includes selecting a first semantic network based on context of the audio signal and more accurately detecting the end-point of user's utterance included in the audio signal based on the first semantic network and also based on at least one timeout threshold associated with the first semantic network.

Abstract: Detecting an end-point of user's voice command or utterance with high accuracy is critical in automatic speech recognition (ASR)-based human machine interface. If an ASR system incorrectly detects an end-point of utterance and transmits this incomplete sentence to other processing blocks for further processing, it is likely the processed result would lead to incorrect interpretation. A method includes selecting a first semantic network based on context of the audio signal and more accurately detecting the end-point of user's utterance included in the audio signal based on the first semantic network and also based on at least one timeout threshold associated with the first semantic network.

Abstract: Detecting an end-point of user's voice command or utterance with high accuracy is critical in automatic speech recognition (ASR)-based human machine interface. If an ASR system incorrectly detects an end-point of utterance and transmits this incomplete sentence to other processing blocks for further processing, it is likely the processed result would lead to incorrect interpretation. A method includes selecting a first semantic network based on context of the audio signal and more accurately detecting the end-point of user's utterance included in the audio signal based on the first semantic network and also based on at least one timeout threshold associated with the first semantic network.

Abstract: Various aspects may include methods, computing devices implementing such methods, and non-transitory processor-readable media storing processor-executable instructions implementing such methods for improving battery life with performance provisioning using machine learning based automated workload classification. Various aspects may include creating a machine learning model based at least in part on computing device metrics, training the machine learning model using performance provisioning rules for work groups; classifying a new work item for a software application into a work group using the trained machine learning model, and applying resource provisioning rules for the work group to the new work item.

Abstract: Various aspects may include methods, computing devices implementing such methods, and non-transitory processor-readable media storing processor-executable instructions implementing such methods for improving battery life with performance provisioning using machine learning based automated workload classification. Various aspects may include creating a machine learning model based at least in part on computing device metrics, training the machine learning model using performance provisioning rules for work groups; classifying a new work item for a software application into a work group using the trained machine learning model, and applying resource provisioning rules for the work group to the new work item.