Abstract: Systems and techniques for acoustic-based linguistically-driven automated text formatting of acoustic input are described herein. An audio sample may be obtained that includes multiple words from human speech. The audio sample may be processed to determine a linguistic relationship of the words from the acoustic properties of the words. Based on the determined linguistic relationships, data such as an acoustical language model is generated to enable an arrangement of the words into a cascade format (e.g., a cascade format that establishes horizontal displacement and vertical displacement among the multiple words). A cascaded text output may be provided from the acoustical language model or other types of related data.

Abstract: Various examples of approaches for using and implementing cascaded text formatting within electronic publishing and display mediums are described herein. In an example, a method for analyzing and re-formatting a stream of text, includes: obtaining a stream of human-readable text in a natural language processing (NLP) parser to identify parts-of-speech and key phrases in a proper sentence; tagging the identified parts-of-speech according to the respective parts and key phrases; generating a text cascade arrangement for a display of the proper sentence by applying predefined rules to the part-of-speech and key phrases, where such a display of the proper sentence provides breaks and cascading, including indentations, according to the pre-defined rules; and outputting an electronic display of the proper sentence which includes the text cascade arrangement.

Abstract: Systems and techniques for linguistically-driven automated text formatting are described herein. Data representing the linguistic structure of input text may be received from Natural Language Processing (NLP) Services, including but not limited to constituents, dependencies, and coreference relationships. A text model of the input text may be built using the linguistic components and relationships. Cascade rules may be applied to the text model to generate a cascaded text data structure. Cascaded data may be displayed on a range of media, including a phone, tablet, laptop, monitor, VR/AR devices. Cascaded data may be presented in dual screen formats to promote more accurate and efficient reading comprehension, greater ease in teaching native and foreign language grammatical structures, and tools for remediation of reading-related disabilities.

Abstract: Systems and techniques for linguistically-driven automated text formatting are described herein. Data representing the linguistic structure of input text may be received from Natural Language Processing (NLP) Services, including but not limited to constituents, dependencies, and coreference relationships. A text model of the input text may be built using the linguistic components and relationships. Cascade rules may be applied to the text model to generate a cascaded text data structure. Cascaded data may be displayed on a range of media, including a phone, tablet, laptop, monitor, VR/AR devices. Cascaded data may be presented in dual screen formats to promote more accurate and efficient reading comprehension, greater ease in teaching native and foreign language grammatical structures, and tools for remediation of reading-related disabilities.