Abstract: In various examples, techniques are described for adapting a multilingual Large Language Model (LLM) into a bilingual Small Language Model (SLM) that exhibits model capacity to understand, process, and generate content in both English and a Low-Resource Language (LRL). The techniques include compressing the LLM to generate a multilingual SLM and performing continued pre-training on the multilingual SLM to generate the bilingual SLM. The techniques also include performing one or more alignment techniques on the bilingual SLM to adapt the SLM's outputs to human values and expectations regarding, e.g., profanity, privacy, politeness, bias, and/or conversational style. The techniques may generate various training corpora, each including one or more of natural English content, natural LRL content, synthetic LRL content generated via translation from English sources, and transliterated synthetic LRL content based on transliterations of natural and/or synthetic LRL content.

Abstract: Approaches presented herein provide for the generation of text transcripts of speech represented in audio data. In particular, an automatic speech recognition (ASR) model can be used together with a retrieval augmented generation (RAG) pipeline to provide for improvement of transcripts that include terminology related, or specific, to a specific knowledge domain. A knowledge base for a given domain can include a number of files or documents in a number of different formats (e.g., documents, images, and webpages) that do not need to be cleaned, classified, or curated. When an ASR generates a transcript where at least one word has a confidence level that falls below a confidence threshold, that transcript can be passed to a language model of the RAG pipeline which can use the retrieved domain-specific data to attempt to identify the appropriate words or terms to use to replace the words tagged as having low confidence.

Abstract: Apparatuses, systems, and techniques are presented to recognize speech in an audio signal. In particular, various embodiments can indicate an end of one or more speech segments based, at least in part, on one or more characters predicted to be within these one or more speech segments.

Abstract: Apparatuses, systems, and techniques are presented to recognize speech in an audio signal. In particular, various embodiments can indicate an end of one or more speech segments based, at least in part, on one or more characters predicted to be within these one or more speech segments.