| Challenge: | Nkululeko is a software tool that lets users perform semi-supervised machine learning experiments in the speaker characteristics domain. |
| Approach: | They propose a software tool called Nkululeko that lets users perform semi-supervised machine learning experiments in the speaker characteristics domain. |
| Outcome: | The proposed tool is based on audformat, a speech database metadata description . it supports best practise and fast setup of experiments without programming skills . |
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| Challenge: | a small fraction of the languages currently covered by speech technologies are mainly spoken in English. |
| Approach: | They present an annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. |
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SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions (2025.findings-emnlp)
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| Challenge: | Existing benchmarks only evaluate a subset of potential conditions, missing others entirely. |
| Approach: | a new benchmark suite evaluates speaker verification models under a variety of stressors . a san francisco-based team evaluates models under natural and background conditions . |
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MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)
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David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei
| Challenge: | (2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results. |
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VoiceBBQ: Investigating Effect of Content and Acoustics in Social Bias of Spoken Language Model (2025.emnlp-main)
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| Challenge: | Due to the nature of speech modality, social bias in Spoken Language Models (SLMs) can emerge from two distinct sources: 1) content aspect and 2) acoustic aspect. |
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Augmenting word2vec with latent Dirichlet allocation within a clinical application (N19-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
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The Nautilus Speaker Characterization Corpus: Speech Recordings and Labels of Speaker Characteristics and Voice Descriptions (L18-1)
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| Challenge: | The Nautilus Speaker Characterization corpus is a conversational microphone speech recording corpus from 300 speakers. |
| Approach: | They present a speaker characterization corpus from 300 german speakers . they use four scripted and four semi-spontaneous dialogs to simulate telephone calls . |
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AfroLID: A Neural Language Identification Tool for African Languages (2022.emnlp-main)
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| Challenge: | AfroLID is a neural LID toolkit for 517 African languages and varieties. |
| Approach: | They propose to exploit a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems to exploit AfroLID. |
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Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications (2020.lrec-1)
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| Challenge: | A speech technology exhibits demographic bias when performance is worse for one demographic group relative to another. |
| Approach: | They create an English dataset of expert-validated audio, transcript> pairs with demographic tags for age, gender, accent and open software which may be used to detect demographic bias in Automatic Speech Recognition systems. |
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Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |