Lightweight and Efficient Spoken Language Identification of Long-form Audio


State-of-the-art Spoken Language Identification (SLI) systems usually focus on tackling short audio clips, and thus their performance degrade drastically when applied to long-form audio, such as podcast, which poses peculiar challenges to existing SLI approaches due to its long duration and diverse content that frequently involves multiple speakers as well as various languages, topics, and speech styles. In this paper, we propose the first system to tackle SLI for long-form audio using podcast data by training a lightweight, multi-class feedforward neural classifier using speaker embeddings as input. We demonstrate that our approach can make inference on long audio input efficiently; furthermore, our system can handle long audio files with multiple speakers and can be further extended into utterance-level inference and code-switching detection, which is currently not covered by any existing SLI system.


May 2024 | The Web Conference

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou

May 2024 | The Web Conference (GFM workshop)

Towards Graph Foundation Models for Personalization

Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas

April 2024 | ICLR

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