Challenge: Few speech resources describe interruption phenomena, especially for TV and media content.
Approach: They propose to annotation Transition-Relevance Places (TRPs) and Floor-Taking event types on an existing French TV and Radio broadcast corpus to facilitate studies of interruptions and turn-taking.
Outcome: The proposed annotations on an existing French TV and Radio broadcast corpus show they are reliable and reliable .

Similar Papers

Annotating Interruption in Dyadic Human Interaction (2022.lrec-1)

Copied to clipboard

Challenge: Existing interruption and turn switch classification methods are not yet available.
Approach: They propose a new interruption annotation schema that integrates existing interruption and turn switch classification methods to annotate different types of interruptions.
Outcome: The proposed method can distinguish smooth turn exchange, backchannel and interruption (including interruption types) and to annotate dyadic conversation.
Overlaps and Gender Analysis in the Context of Broadcast Media (2022.lrec-1)

Copied to clipboard

Challenge: Using gender and overlap annotations, we characterise interactions between speakers according to their gender and role in broadcast media.
Approach: They propose to characterise interactions between speakers according to their gender and role in broadcast media by using a small dataset of 93 recordings from LCP French channel.
Outcome: The proposed method could improve the efficiency of qualitative studies conducted in human sciences.
A Conversation-Analytic Annotation of Turn-Taking Behavior in Japanese Multi-Party Conversation and its Preliminary Analysis (2020.lrec-1)

Copied to clipboard

Challenge: a new conversation-analytic annotation scheme is proposed for multi-party conversations . current systems do not take a turn like a human even in simple two-party conversation .
Approach: They propose a conversation-analytic annotation scheme for turn-taking behavior in multi-party conversations . they analyze how syntactic and prosodic features of utterances vary across four selection types .
Outcome: The proposed model is based on Japanese multi-party conversations.
MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations (2024.acl-short)

Copied to clipboard

Challenge: a new problem setting is designed to detect critical moments in conversations . a human-annotated multi-modal dataset is used to classify and detect turning points .
Approach: They propose a problem setting focusing on turning points in conversations as TPs . they propose MTPC, MTPD, & MTPR tasks to classify and detect turning points .
Outcome: The proposed model achieves an F1-score of 0.88 in classification and 0.61 in detection . it uses state-of-the-art vision-language models to construct a narrative from the videos .
Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances (2022.naacl-main)

Copied to clipboard

Challenge: Existing approaches for recognizing feature transitions between utterances extract features for the context at the coarse-grained level.
Approach: They propose a method to recognize feature transitions between utterances that helps understand dialogue flow . they propose empathetic response generation strategy to focus on emotion and keywords related to appropriate features when generating responses.
Outcome: The proposed approach outperforms baseline approaches and improves on multi-turn dialogues.
ALLIES: A Speech Corpus for Segmentation, Speaker Diarization, Speech Recognition and Speaker Change Detection (2024.lrec-main)

Copied to clipboard

Challenge: a meta corpus of audio files is used to gather, annotate and transcribe speech . a large number of speech databases are needed to perform multi-speaker tasks such as speaker diarization and speaker change detection.
Approach: They propose to use human feedback to homogenize and correct speaker labels among the audio files by integrating human feedback within a speaker verification system.
Outcome: The proposed protocol evaluates speech segmentation, speaker diarization, speech transcription and speaker change detection using human feedback.
Contribution of Move Structure to Automatic Genre Identification: An Annotated Corpus of French Tourism Websites (2024.lrec-main)

Copied to clipboard

Challenge: a concept of move structure has been overlooked in genre analysis, but it is not widely used in natural language processing.
Approach: They propose to incorporate move structure into a neural architecture for automatic genre identification.
Outcome: The proposed approach can increase performance and reduce computational power.
Towards a Conversation-Analytic Taxonomy of Speech Overlap (L18-1)

Copied to clipboard

Challenge: a taxonomy for classifying speech overlap in natural language dialogue is presented . the scheme classifies overlap on the basis of several features, including onset point, local dialogue history, and management behavior.
Approach: They propose a taxonomy for classifying speech overlap in natural language dialogue . they describe the various dimensions of the scheme and show how it was applied to a corpus of collaborative dialogue based on onset point, dialogue history, and management behavior .
Outcome: The proposed taxonomy classifies overlap on the basis of onset point, dialogue history, management behavior.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations (D19-3)

Copied to clipboard

Challenge: Proceedings of the system demonstrations session were presented at the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) EMNMP-IjCNLP 2019 has a Best Demo Award for the first time .
Approach: Proceedings of the system demonstrations session are available online . they were presented at the conference on empirical methods in natural language processing .
Outcome: The system demonstrations session received 110 submissions, 22 of which were either invalid or withdrawn by the authors.
Querying Interaction Structure: Approaches to Overlap in Spoken Language Corpora (2022.lrec-1)

Copied to clipboard

Challenge: In this paper, we address two specific problems arising when indexing and searching interaction corpora with overlapping speaker contributions.
Approach: They propose and experiment with a speaker-based search mode that enables any speaker’s transcription tier to be the basic tokenization layer whereby contributions of other speakers are mapped to this given tier.
Outcome: The proposed method enables any speaker’s transcription tier to be the basic tokenization layer whereby contributions of other speakers are mapped to this given tier.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations