Abstract: A system for performing automated speech recognition (ASR) on audio data includes a queue manager to receive a request to perform ASR on audio data, add the request to a queue of incoming requests, and determine a queue depth representing a number of requests in the queue at a given time. The system also includes a load supervisor to receive the request and the queue depth from the queue manager and assign a service level for the request based on the queue depth. In addition, the system includes a speech-to-text converter to receive the assigned service level for the request from the load supervisor, select an ASR model for the request based on the received service level, receive the audio data associated with the request, and perform ASR on the audio data using the selected ASR model.

Abstract: A system for performing automated speech recognition (ASR) on audio data includes a queue manager to receive a request to perform ASR on audio data, add the request to a queue of incoming requests, and determine a queue depth representing a number of requests in the queue at a given time. The system also includes a load supervisor to receive the request and the queue depth from the queue manager and assign a service level for the request based on the queue depth. In addition, the system includes a speech-to-text converter to receive the assigned service level for the request from the load supervisor, select an ASR model for the request based on the received service level, receive the audio data associated with the request, and perform ASR on the audio data using the selected ASR model.

Abstract: Provided is a system and method for generating at least one aspect associated with a future event for a subject using historical data. The method including: determining a subject embedding using a recurrent neural network (RNN), input to the RNN includes historical events of the subject from the historical data, each historical event including by an aspect embedding, the RNN trained using aspects associated with events of similar subjects from the historical data; generating at least one aspect of the future event for the subject using a generative adversarial network (GAN), input to the GAN includes the subject embedding, the GAN trained with subject embeddings determined using the RNN for other subjects in the historical data; and outputting the at least one generated aspect.

Abstract: A portable suction system comprises a vacuum valve subassembly. The subassembly has a body, upper and lower ball valves, and an actuating linkage. The body has a mounting portion for a tank and a valve housing portion. The mounting portion has a threaded diameter. The tank mounting portion has venturi and feed passageways. The valve housing portions have a cylindrical configurations. The body has a venturi blow-through line passageway intersecting. The ball valves are located within the passageways of the valve body. The ball valves are coupled to the actuating linkage with a trigger which rotates the ball valves into one of the positions from the category. The category includes the venturi ball valve open and feed ball valve open, and neither ball valve open. The linkage is coupled to and held in position on the outside surface of the valve body.