Abstract: Systems and methods provide for a machine learning system to train a machine learning model to output a penalty-free emission when processing an auditory input. For example, as the system generates paths through a probability lattice, one or more paths may include a penalty-free emission that skips at least one frame associated with the probability lattice, but that does not add a cost to a final path cost. The use of the penalty-free emissions may be represented through one or more graphical representations used for training in order to develop loss functions for models. One or more of these frameworks may be incorporated into automatic speech recognition pipelines to improve training while also reducing coding requirements to simplify debugging operations.

Abstract: In various examples, a technique for performing a mathematical reasoning task includes inputting a first prompt that includes (i) a set of example mathematical problems, (ii) example masked solutions to the example mathematical problems, and (iii) a mathematical problem into a first machine learning model, wherein each masked solution includes a set of symbols as substitutes for a set of numbers in a ground-truth solution for a corresponding example mathematical problem. The technique also includes generating, via execution of the first machine learning model based on the first prompt, a set of candidate masked solutions to the mathematical problem. The technique further includes inputting a second prompt that includes (i) the mathematical problem and (ii) at least one masked solution into a second machine learning model and generating, via execution of the second machine learning model based on the second prompt, a solution to the mathematical problem.

Abstract: Systems and methods provide for a machine learning system to train a machine learning model to output a penalty-free emission when processing an auditory input. For example, as the system generates paths through a probability lattice, one or more paths may include a penalty-free emission that skips at least one frame associated with the probability lattice, but that does not add a cost to a final path cost. The use of the penalty-free emissions may be represented through one or more graphical representations used for training in order to develop loss functions for models. One or more of these frameworks may be incorporated into automatic speech recognition pipelines to improve training while also reducing coding requirements to simplify debugging operations.

Abstract: Systems and methods provide for a machine learning system to train a machine learning model to output a penalty-free emission when processing an auditory input. For example, as the system generates paths through a probability lattice, one or more paths may include a penalty-free emission that skips at least one frame associated with the probability lattice, but that does not add a cost to a final path cost. The use of the penalty-free emissions may be represented through one or more graphical representations used for training in order to develop loss functions for models. One or more of these frameworks may be incorporated into automatic speech recognition pipelines to improve training while also reducing coding requirements to simplify debugging operations.