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Speech Emotion Recognition using Attention-based LSTM-Network with Residual Connection

arXiv:2606.03359v1 Announce Type: new Abstract: Speech emotion recognition is an important component of modern human-computer interaction systems. However, many state-of-the-art approaches rely on large pretrained models with high computational and memory requirements, limiting their applicability. This paper proposes ResLSTM-SA, a lightweight architecture that integrates residual connections with soft attention within an LSTM-based framework.

arXiv CS 7d ago

Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognition

arXiv:2606.07309v1 Announce Type: new Abstract: Instruction-following audio language models (ALMs) can be augmented with explicit acoustic cues, yet it remains unclear whether such cues are used in a grounded way when the raw audio is already available. We study this question in speech emotion recognition (SER) by deriving six interpretable acoustic concept tokens from the standardised eGeMAPS paralinguistic feature set. These tokens summarise energy, pitch, dynamics, brightness, formants,...

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