Tania Jiménez
Tania Jiménez has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Alix Dupont
LIA/CERI, Avignon University, Avignon, France
1 shared publication - Eric SanJuan
Avignon Université, LIA
1 shared publication Jean-Baptiste Bréal
SYSTEM Department, EDF Lab Paris-Saclay, EDF Research and Development Group, Palaiseau, France
1 shared publication- Jean-François Bonastre
Avignon Universite Laboratoire Informatique d'Avignon, Avignon, France and Defense and Security dept, Inria, Paris, France
1 shared publication - Jeanne Vermeirsche1 shared publication
- Linda Salahaldin
OMNES Education
1 shared publication
Latest Publications
- 4 July 2026
- Published by Springer Nature
- p. 252-267
Evaluating LLM-Generated Wikipedia Content: Political Topics in a French Setting
- 4 July 2026
- Published by Springer Nature
- p. 252-267
Evaluating LLM-Generated Wikipedia Content: Political Topics in a French Setting
- 1 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Engineering Management
- Vol. 73, 4086-4106
Learn, Predict and Reschedule: Toward a Data-Driven Crowdfunding Platform Management
- 1 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Engineering Management
- Vol. 73, 4086-4106
Learn, Predict and Reschedule: Toward a Data-Driven Crowdfunding Platform Management
- 1 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Intelligent Transportation Systems
- Vol. 27 (10) , 1-12
Dynamic Parking Pricing for Electric Vehicles in a Loss Queue
- 1 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Intelligent Transportation Systems
- Vol. 27 (10) , 1-12
Dynamic Parking Pricing for Electric Vehicles in a Loss Queue
- Open Access
- 23 November 2024
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Asian and Low-Resource Language Information Processing
- Vol. 23 (12) , 1-11
Improving Tone Recognition Performance using Wav2vec 2.0-Based Learned Representation in Yoruba, a Low-Resourced Language
Many sub-Saharan African languages are categorized as tone languages, and for the most part, they are classified as low-resource languages due to the limited resources and tools available to process these languages. Identifying the tone associated with a syllable is therefore a key challenge for speech recognition in these languages. We propose models that automate the recognition of tones in continuous speech that can easily be incorporated into a speech recognition pipeline for these languages. We have investigated different neural architectures as well as several feature extraction algorithms in speech (FBs (Filter Banks), LEAF (Learnable Frontend), CS (Cestrogram), MFCC (Mel-Frequency Cepstral Coefficients)). In the context of low-resource languages, we also evaluated W2V (Wav2vec 2.0) models for this task. In this work, we use a public speech recognition dataset on Yoruba. As for the results, using the combination of features obtained from CS and FBs, we obtain a minimum TER (Tone Error Rate) of 19.54%, whereas for the evaluations of the models using W2V, we have a TER of 17.72%, demonstrating that the use of W2V provides better performance than the models used in the literature for tone identification on low-resource languages.
- 23 November 2024
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Asian and Low-Resource Language Information Processing
- Vol. 23 (12) , 1-11
Open AccessImproving Tone Recognition Performance using Wav2vec 2.0-Based Learned Representation in Yoruba, a Low-Resourced Language
Many sub-Saharan African languages are categorized as tone languages, and for the most part, they are classified as low-resource languages due to the limited resources and tools available to process these languages. Identifying the tone associated with a syllable is therefore a key challenge for speech recognition in these languages. We propose models that automate the recognition of tones in continuous speech that can easily be incorporated into a speech recognition pipeline for these languages. We have investigated different neural architectures as well as several feature extraction algorithms in speech (FBs (Filter Banks), LEAF (Learnable Frontend), CS (Cestrogram), MFCC (Mel-Frequency Cepstral Coefficients)). In the context of low-resource languages, we also evaluated W2V (Wav2vec 2.0) models for this task. In this work, we use a public speech recognition dataset on Yoruba. As for the results, using the combination of features obtained from CS and FBs, we obtain a minimum TER (Tone Error Rate) of 19.54%, whereas for the evaluations of the models using W2V, we have a TER of 17.72%, demonstrating that the use of W2V provides better performance than the models used in the literature for tone identification on low-resource languages.