Efficient Spoken Language Recognition via Multilabel Classification

• URL: http://arxiv.org/abs/2306.01945v1
• Date: Fri, 2 Jun 2023 23:04:19 GMT
• Title: Efficient Spoken Language Recognition via Multilabel Classification
• Authors: Oriol Nieto, Zeyu Jin, Franck Dernoncourt, Justin Salamon
• Abstract summary: We show that our models obtain competitive results while being orders of magnitude smaller and faster than current state-of-the-art methods. Our multilabel strategy is more robust to unseen non-target languages compared to multiclass classification.
• Score: 53.662747523872305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spoken language recognition (SLR) is the task of automatically identifying the language present in a speech signal. Existing SLR models are either too computationally expensive or too large to run effectively on devices with limited resources. For real-world deployment, a model should also gracefully handle unseen languages outside of the target language set, yet prior work has focused on closed-set classification where all input languages are known a-priori. In this paper we address these two limitations: we explore efficient model architectures for SLR based on convolutional networks, and propose a multilabel training strategy to handle non-target languages at inference time. Using the VoxLingua107 dataset, we show that our models obtain competitive results while being orders of magnitude smaller and faster than current state-of-the-art methods, and that our multilabel strategy is more robust to unseen non-target languages compared to multiclass classification.

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