Abstract: Methods and systems for recognizing speech are disclosed herein. A method can include performing blank filtering on a received speech input to generate a plurality of filtered encodings and processing the plurality of filtered encodings to generate a plurality of audio embeddings. The method can also include mapping each audio embedding of the plurality of audio embeddings to a textual embedding using a speech adapter to generate a plurality of combined embeddings and receiving one or more specific textual embeddings from a domain-specific entity retriever based on the plurality of filtered encodings. The method can further include providing plurality of combined embeddings and the one or more specific textual embeddings to a machine-trained model and receiving a textual output representing speech from the speech input from the machine-trained model.

Abstract: Described herein relates to a self-supervised reinforced learning (hereinafter “SSRL”) system for question-and-answer query systems that may include one or more initial steps to prime a neural network to prevent an agent from prematurely selecting an early path and method thereof. The SSRL method may also provide the agent with a lead for queries with a large action space, such that at least one existing reinforced learning system may be improved (e.g., accuracy). Additionally, to scale to larger datasets in which label generation for each training sample is infeasible, the SSRL system may include one or more steps of pretraining the dataset(s) with partial labels, which may be generated from a subset of a whole knowledge graph.