MSP-Podcast

The MSP-Podcast dataset is a benchmark that contains in-the-wild speech data used to evaluate the effectiveness of speech emotion conversion methods.

Papers using MSP-Podcast (16)

Multistage Linguistic Conditioning Of Convolutional Layers For Speech Emotion Recognition2021 · 16 citesTargetSEC: Plug-and-Play In-the-Wild Speech Emotion Conversion via Arousal-Conditioned Latent Style Diffusion2026AdaLTM: Adaptive Layer-wise Task Vector Merging for Categorical Speech Emotion Recognition with ASR Knowledge Integration2026Joint Learning using Mixture-of-Expert-Based Representation for Speech Enhancement and Robust Emotion Recognition2025Enhancing In-the-Wild Speech Emotion Conversion with Resynthesis-based Duration Modeling2025Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion Techniques2024 · 10 citesMultistage linguistic conditioning of convolutional layers for speech emotion recognition2021 · 7 citesMSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition2024 · 6 citesSentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition2022 · 1 citesIn-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis2023 · 1 citesPersonalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition2023 · 1 citesRepresentation learning through cross-modal conditional teacher-student training for speech emotion recognition2021Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations2022Dawn of the transformer era in speech emotion recognition: closing the valence gap2022emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition2024Fusion approaches for emotion recognition from speech using acoustic and text-based features2024