Automatic speech recognition (ASR) systems have seen substantial improvements in the past decade, in particular with the advent of Self-supervised learning speech models; however, ASR systems do not recognize the speech of everyone equally well. Recent research shows that bias exists against different types of speech, including non-native and regional accents [5] [6], in state-of-the-art ASR systems. To attain robust speech recognition regardless of speakers’ accents, bias mitigation is necessary [8], [9].
The goal of this internship is twofold: (1) quantifying bias in speech recognition of English accented speech for widely used pre-trained speech models and across different model sizes; (2) exploring methods for mitigating accent bias and implementing models that yield improved performance on accented speech recognition.
The intern for this position is expected to perform the following tasks:
This internship is part of an ANR project called DIKÉ (https://www.anr-dike.fr/), which aims at studying bias, fairness and ethics of compressed NLP models. Results are expected to be reported in a paper by the end of the internship (or soon after). Improved models are also expected to be shared with the scientific community through HuggingFace models hub.
Supervisors: Caroline Brun, Salah Ait-Mokhtar and Nikolaos Lagos.
- PhD or last year MSc student in NLP or speech processing
- Solid deep learning and NLP/speech processing background
- Advanced expertise in neural network architectures
- Excellent programming skills in Python and proficiency in PyTorch
[1] Baevski, Alexei, et al. "wav2vec 2.0: A framework for self-supervised learning of speech representations." Advances in neural information processing systems 33 (2020): 12449-12460.
[2] Hsu, Wei-Ning, et al. "Hubert: Self-supervised speech representation learning by masked prediction of hidden units." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 3451-3460.
[3] Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022). Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356.
[4] Siyin Wang and Chao-Han Huck Yang and Ji Wu and Chao Zhang. “Can Whisper perform speech-based in-context learning?” Proceedings of ICASSP2024, Seoul, Korea, 2024.
[5] Feng, Siyuan and Halpern, Bence Mark and Kudina, Olya and Scharenborg, Odette. “Towards inclusive automatic speech recognition”, Computer Speech and Languages, Vol. 84, 2024.
[6] Koenecke A., Nam A., Lake E., Nudell J., Quartey M., Mengesha Z., Toups C., Rickford J.R., Jurafsky D., Goel S. “Racial disparities in automated speech recognition”. Proc. Natl. Acad. Sci., 117 (14) (2020), pp. 7684-7689.
[7] Yuanyuan Zhang and Aaricia Herygers and Tanvina Patel and Zhengjun Yue and Odette Scharenborg. “Exploring data augmentation in bias mitigation against non-native-accented speech”, arXiv: 2312.15499.
[8] Darshan Prabhu, Preethi Jyothi, Sriram Ganapathy, and Vinit Unni. 2023. “Accented Speech Recognition With Accent-specific Codebooks”. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7175–7188, Singapore. Association for Computational Linguistics.
[9] Juan Zuluaga-Gomez and Sara Ahmed and Danielius Visockas and Cem Subakan. “CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice”. arXiv:2305.18283
Please note that applicants must be registered students at a university or other academic institution and that this establishment will need to sign an 'Internship Convention' with NAVER LABS Europe before the student is accepted.
You can apply for this position online. Don't forget to upload your CV and cover letter before you submit. Incomplete applications will not be accepted.
NAVER is the #1 Internet portal in Korea with activities that span a wide range of businesses including search, commerce, content, financial and cloud platforms.
NAVER LABS, co-located in Korea and France, is the organization dedicated to preparing NAVER’s future. NAVER LABS Europe is located in a spectacular setting in Grenoble, in the heart of the French Alps. Scientists at NAVER LABS Europe are empowered to pursue long-term research problems that, if successful, can have significant impact and transform NAVER. We take our ideas as far as research can to create the best technology of its kind. Active participation in the academic community and collaborations with world-class public research groups are, among others, important tools to achieve these goals. Teamwork, focus and persistence are important values for us.
NAVER LABS Europe is an equal opportunity employer.
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
—————
Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
——————-
Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
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