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Multimodal pre-train then transfer learning approach for speaker recognition

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Abstract

Cognitive science has well-established the correlation between faces and voices because neuro-cognitive pathways of both information share the same structure. Recently, the task has come to the attention of the computer vision community with the introduction of large-scale face-voice data. To this end, our work aims to leverage the structure of faces and voices along with the availability of large-scale face-voice information to improve speaker recognition tasks including identification and verification. To achieve this task, we propose novel multimodal systems to leverage the structure of face and voice, one with weight sharing and another without weight sharing, to learn joint representations of multiple modalities establishing the Face-voice association. Afterwards, features are extracted from the trained multimodal networks capturing face-voice association to perform speaker recognition tasks. We evaluated our proposed multimodal networks for speaker recognition along with Face-voice association tasks on challenging benchmark datasets including VoxCeleb1 and MAV-Celeb. Our results show that adding facial information improved speaker recognition tasks’ performance.

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Data Availibility

In our experiments, we use VoxCeleb1 [3] dataset to evaluate the proposed method. VoxCeleb1 is gender balanced, with 55% of the speakers male. The speakers span a wide range of different ethnicities, accents, professions and ages. Moreover, data privacy notice is available on the official website of the dataset [69]. Specifically, we extract face embeddings using Inception-ResNet-V1 trained with triplet loss, similar to the work of Schroff et al. [62]. We extract audio embeddings (\(\textbf{e}_i\)) using an utterance level aggregator [34] trained on speaker recognition task.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang Univeristy, Hangzhou, 310007, Zhejiang, China

    Summaira Jabeen & Xi Li

  2. School of Software Engineering, East China Normal University, Shanghai, 200062, China

    Muhammad Shoib Amin

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  1. Summaira Jabeen
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Correspondence to Summaira Jabeen.

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Jabeen, S., Amin, M.S. & Li, X. Multimodal pre-train then transfer learning approach for speaker recognition. Multimed Tools Appl 83, 78563–78576 (2024). https://doi.org/10.1007/s11042-024-18575-4

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