Abstract: Some disclosed embodiments are directed to obtaining a decoded audio data including a spoken language utterance recognized in audio data and identifying a disfluency in the decoded audio data. Upon determining that correcting the disfluency would improve a readability score of the decoded audio data, the system generates a particular correction to correct the disfluency and applies the particular correction to the decoded audio data. Then, an updated decoded audio data is generated which reflects the particular correction. The updated decoded audio data has improved readability over the decoded audio data.

Abstract: Systems are configured to obtain streaming audio data comprising language utterances, continuously decode the streaming audio data in order to generate decoded streaming audio data and determine whether a linguistic boundary exists within an initial segment of decoded streaming audio data. When a linguistic boundary is determined to exist, the systems apply a punctuation at the linguistic boundary and output a first portion of the initial segment of the streaming audio data ending at the linguistic boundary while refraining from outputting a second portion of the initial segment which is located temporally subsequent to the first portion of the initial segment. Systems are also configured to delay the output until predetermined punctuation validation processes have been performed.

Abstract: Solutions for custom display post processing (DPP) in speech recognition (SR) use a customized multi-stage DPP pipeline that transforms a stream of SR tokens from lexical form to display form. A first transformation stage of the DPP pipeline receives the stream of tokens, in turn, by an upstream filter, a base model stage, and a downstream filter, and transforms a first aspect of the stream of tokens (e.g., disfluency, inverse text normalization (ITN), capitalization, etc.) from lexical form into display form. The upstream filter and/or the downstream filter alter the stream of tokens to change the default behavior of the DPP pipeline into custom behavior. Additional transformation stages of the DPP pipeline perform further transforms, allowing for outputting final text in a display format that is customized for a specific user. This permits each user to efficiently leverage a common baseline DPP pipeline to produce a custom output.

Abstract: Systems generate segments of spoken language utterances based on different sets of segmentation boundaries. The systems are also configured to generate one or more formatted segments by assigning a punctuation tags at segmentation boundaries and to generate one or more final sentences from the one or more segments.

Abstract: Generally discussed herein are devices, systems, and methods for multi-lingual model generation. A method can include determining, for low-resource languages, respective a language similarity value indicating language similarity between each of the low-resource languages, clustering the low-resource languages into groups based on the respective language similarity value, aggregating training data of languages corresponding to a given group resulting in aggregated training data, and training a re-ranking language model based on the aggregated training data resulting in a trained re-ranking language model.

Abstract: Solutions for on-device streaming inverse text normalization (ITN) include: receiving a stream of tokens, each token representing an element of human speech; tagging, by a tagger that can work in a streaming manner (e.g., a neural network), the stream of tokens with one or more tags of a plurality of tags to produce a tagged stream of tokens, each tag of the plurality of tags representing a different normalization category of a plurality of normalization categories; based on at least a first tag representing a first normalization category, converting, by a first language converter of a plurality of category-specific natural language converters (e.g., weighted finite state transducers, WFSTs), at least one token of the tagged stream of tokens, from a first lexical language form, to a first natural language form; and based on at least the first natural language form, outputting a natural language representation of the stream of tokens.