arXiv subjects

Ameenudeen P E

Publications and source records attributed to Ameenudeen P E.

2 recordsLinked to original sources

arXiv2026

JASPER: Joint Audio and Speech Pre-trained Encoder Representations

Self-supervised learning (SSL) for speech and audio has largely progressed along separate tracks: speech models emphasise time-domain prediction, whereas audio representation learning has focused on time-frequency patterns. This separation creates a compatibility gap, limiting cross-domain generalization. In this work, we introduce JASPER, Joint Audio and Speech Pre-trained Encoder Representations, a framework that augments speech-pretrained models with time-frequency objectives. Specifically, JASPER performs masked prediction of temporal and spectral targets over long audio segments, enabling spectro-temporal representation learning of speech and audio signals. The proposed method consistently outperforms multiple baselines and existing speech/audio encoders on diverse speech, audio, and music tasks, demonstrating the effectiveness of unified spectro-temporal modeling.

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arXiv2026

ULTRAS -- Unified Learning of Transformer Representations for Audio and Speech Signals

Self-supervised learning (SSL) has driven impressive advances in speech processing by adopting time-domain prediction objectives, while audio representation learning frameworks operate on time-frequency spectrograms. Models optimized for one paradigm struggle to transfer to the other, highlighting the need for a joint framework. We propose Unified Learning of Transformer Representations for Audio and Speech (ULTRAS), where the masking and predictive modeling is performed over long patches of the data. The model, based on the transformer architecture, encodes spectral-patches of log-mel spectrogram features. The predictive modeling of masked segments is performed on spectral and temporal targets using a combined loss-function, forcing the representations to encode time and frequency traits. Experiments are performed on a variety of speech and audio tasks, where we illustrate that the ULTRAS framework achieves improved performance over other established baselines.

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