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Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
Loren Lugosch · Piyush Papreja · Mirco Ravanelli · Abdelwahab HEBA · Titouan Parcollet

This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers. We describe the gap in existing spoken language understanding datasets that Timers and Such fills, the design and creation of the dataset, and experiments with a number of ASR-based and end-to-end baseline models, the code for which has been made available as part of the SpeechBrain toolkit.

Author Information

Loren Lugosch (Mila)
Piyush Papreja (Arizona State University)
Mirco Ravanelli (Montreal Istitute for Learning Algorithms)

I received my master's degree in Telecommunications Engineering (full marks and honours) from the University of Trento, Italy in 2011. I then joined the SHINE research group (led by Prof. Maurizio Omologo) of the Bruno Kessler Foundation (FBK), contributing to some projects on distant-talking speech recognition in noisy and reverberant environments, such as DIRHA and DOMHOS. In 2013 I was visiting researcher at the International Computer Science Institute (University of California, Berkeley) working on deep neural networks for large-vocabulary speech recognition in the context of the IARPA BABEL project (led by Prof. Nelson Morgan). I received my PhD (with cum laude distinction) in Information and Communication Technology from the University of Trento in December 2017. During my PhD I worked on “deep learning for distant speech recognition”, with a particular focus on recurrent and cooperative neural networks (see my PhD thesis here). In the context of my PhD I recently spent 6 months in the MILA lab led by Prof. Yoshua Bengio. I'm currently a post-doc researcher at the University of Montreal, working on deep learning for speech recognition in the MILA Lab.

Abdelwahab HEBA (INSTITUT DE RECHERCHE EN INFORMATIQUE DE TOULOUSE)
Titouan Parcollet (None)

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