| 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. |
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| 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. |
Querying Interaction Structure: Approaches to Overlap in Spoken Language Corpora (2022.lrec-1)
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| 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. |
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ALLIES: A Speech Corpus for Segmentation, Speaker Diarization, Speech Recognition and Speaker Change Detection (2024.lrec-main)
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| 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. |
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Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
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| Challenge: | Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. |
| Approach: | They propose to use probing methods to assess gender encoding across ST models. |
| Outcome: | The proposed models capture speaker-specific features, including gender, while older models do not . low gender encoding capabilities result in systems’ tendency toward a masculine default, a translation bias that is more pronounced in newer architectures. |
Automatically Inferring Gender Associations from Language (D19-1)
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| Challenge: | In this paper, we demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. |
| Approach: | They propose to integrate two datasets and a novel approach to automatically infer gender associations from language and find coherent word clusters and label clusters for the semantic concepts they represent. |
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Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation (2022.acl-long)
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| Challenge: | grammatical gender languages are characterized by morphosyntactic chains of gender agreement marked on a variety of lexical items and parts-of-speech (POS). |
| Approach: | They propose to enrich the natural, gender-sensitive MuST-SHE corpus with two new linguistic annotation layers to explore gender bias. |
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Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus (2020.acl-main)
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| Challenge: | a growing number of studies have examined the issue of gender bias in speech translation . a gender bias is a systemic problem that reproduces gender stereotypes discriminating women. |
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How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation (2021.findings-acl)
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| Challenge: | Existing methods for subword splitting penalize the representation of feminine linguistic markings. |
| Approach: | They propose a method that preserves subword splitting while leveraging character-based segmentation to properly translate gender. |
| Outcome: | The proposed approach preserves BPE overall translation quality while leveraging the higher ability of character-based segmentation to properly translate gender. |
Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)
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| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
| Approach: | They explore word embedding stability in a wide range of languages to gain insight into their stability. |
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Examining Gender Bias in Languages with Grammatical Gender (D19-1)
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| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
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