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The prominence of a spoken word is the degree to which an average native listener perceives the word as salient or emphasized relative to its context. Speech prominence estimation is the process of assigning a numeric value to the prominence of each word in an utterance. These prominence labels are useful for linguistic analysis, as well as training automated systems to perform emphasis-controlled text-to-speech or emotion recognition. Manually annotating prominence is time-consuming and expensive, which motivates the development of automated methods for speech prominence estimation.

The prominence of a spoken word is the degree to which an average native listener perceives the word as salient or emphasized relative to its context. Speech prominence estimation is the process of assigning a numeric value to the prominence of each word in an utterance. These prominence labels are useful for linguistic analysis, as well as training automated systems to perform emphasis-controlled text-to-speech or emotion recognition. Manually annotating prominence is time-consuming and expensive, which motivates the development of automated methods for speech prominence estimation.

Relation extraction (RE) is a vital task within natural language processing. Previous works predominantly focus on extracting relations from plain text. However, with the evolution of communication habits, many individuals employ symbolic representations, e.g. emoticons, to convey nuanced information. This shift in communication prompts a pertinent question: How do emoticons impact the performance of RE models?

Listening to long video/audio recordings from video conferencing and online courses for acquiring information is extremely inefficient. Even after ASR systems transcribe recordings into long-form spoken language documents, reading ASR transcripts only partly speeds up seeking information. It has been observed that a range of NLP applications, such as keyphrase extraction, topic segmentation, and summarization, significantly improve users' efficiency in grasping important information.

As part of an ongoing research into extracting mission-critical information from Search and Rescue speech communications, a corpus of unscripted, goal-oriented, two-party spoken conversations has been designed and collected. The Sheffield Search and Rescue (SSAR) corpus comprises about 12 hours of data from 96 conversations by 24 native speakers of British English with a southern accent. Each conversation is about a collaborative task of exploring and estimating a simulated indoor environment.

This study examines the challenging issues in the semantic annotation of the characteristics of verbal information of Mandarin Chinese. It proposes a frame-based constructional approach that aligns with linguistic premises in Frame Semantics, Construction Grammar and Cognitive Grammar. Given that semantic processing has a lot to do with human cognitive capacities, semantic transfer and profile on the basis of natural inferences of event chains have to be considered in verb categorization and representation.

Speech discourse comprehension is crucial for developing intelligent speech processing technologies. The present research aims to establish a multi-layered annotation scheme for Chinese discourse that contains inter-related information of phonetics, phonology, syntax, semantics and pragmatics. This research provides a theoretical foundation and analytical support for discourse comprehension by examining and modelling the relationships between prosody and morphology-syntax, as well as semantics and other structures during speech interactions.