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arXiv · 2410.05423

Incorporating Talker Identity Aids With Improving Speech Recognition in Adversarial Environments

Sagarika Alavilli·Annesya Banerjee·Gasser Elbanna·Annika Magaro
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Abstract

Current state-of-the-art speech recognition models are trained to map acoustic signals into sub-lexical units. While these models demonstrate superior performance, they remain vulnerable to out-of-distribution conditions such as background noise and speech augmentations. In this work, we hypothesize that incorporating speaker representations during speech recognition can enhance model robustness to noise. We developed a transformer-based model that jointly performs speech recognition and speaker identification. Our model utilizes speech embeddings from Whisper and speaker embeddings from ECAPA-TDNN, which are processed jointly to perform both tasks. We show that the joint model performs comparably to Whisper under clean conditions. Notably, the joint model outperforms Whisper in high-noise environments, such as with 8-speaker babble background noise. Furthermore, our joint model excels in handling highly augmented speech, including sine-wave and noise-vocoded speech. Overall, these results suggest that integrating voice representations with speech recognition can lead to more robust models under adversarial conditions.

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Sagarika Alavilli, Annesya Banerjee, Gasser Elbanna, Annika Magaro. 2024-10-07. Incorporating Talker Identity Aids With Improving Speech Recognition in Adversarial Environments. https://arxiv.org/abs/2410.05423

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