# The MSP-Podcast Corpus

arXiv: https://arxiv.org/abs/2509.09791 · HTML: https://mlsanity.com/paper/2509.09791/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2025.

## Built on by (most cited first, 16 of 16)

- [Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox](https://mlsanity.com/paper/2605.27772/) (2026)
- [What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels](https://mlsanity.com/paper/2512.16832/) (2025)
- [NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion](https://mlsanity.com/paper/2511.00256/) (2025)
- [VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs](https://mlsanity.com/paper/2603.08936/) (2026)
- [The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation](https://mlsanity.com/paper/2604.26347/) (2026)
- [AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech](https://mlsanity.com/paper/2605.17583/) (2026)
- [Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning](https://mlsanity.com/paper/2606.00851/) (2026)
- [AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models](https://mlsanity.com/paper/2610.01560/) (2026)
- [Improving Speech Emotion Recognition with Mutual Information Regularized Generative Model](https://mlsanity.com/paper/2510.10078/) (2025)
- [Recovering Performance in Speech Emotion Recognition from Discrete Tokens via Multi-Layer Fusion and Paralinguistic Feature Integration](https://mlsanity.com/paper/2601.17085/) (2026)
- [ADEPT: RL-Aligned Agentic Decoding of Emotion via Evidence Probing Tools -- From Consensus Learning to Ambiguity-Driven Emotion Reasoning](https://mlsanity.com/paper/2602.12714/) (2026)
- [Prompt Amplification and Zero-Shot Late Fusion in Audio-Language Models for Speech Emotion Recognition](https://mlsanity.com/paper/2603.23057/) (2026)
- [Multimodal Hidden Markov Models for Persistent Emotional State Tracking](https://mlsanity.com/paper/2605.12838/) (2026)
- [Learning from Annotation Uncertainty: Entropy-Aware Curriculum for Speech Emotion Recognition](https://mlsanity.com/paper/2606.27536/) (2026)
- [Emotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation Models](https://mlsanity.com/paper/2608.17102/) (2026)
- [Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity](https://mlsanity.com/paper/2610.15181/) (2026)

## Abstract (arXiv)

The availability of large, high-quality emotional speech databases is essential for advancing speech emotion recognition (SER) in real-world scenarios. However, many existing databases face limitations in size, emotional balance, and speaker diversity. This study describes the MSP-Podcast corpus, summarizing our ten-year effort. The corpus consists of over 400 hours of diverse audio samples from various audio-sharing websites, all of which have Common Licenses that permit the distribution of the corpus. We annotate the corpus with rich emotional labels, including primary (single dominant emotion) and secondary (multiple emotions perceived in the audio) emotional categories, as well as emotional attributes for valence, arousal, and dominance. At least five raters annotate these emotional labels. The corpus also has speaker identification for most samples, and human transcriptions of the lexical content of the sentences for the entire corpus. The data collection protocol includes a machine learning-driven pipeline for selecting emotionally diverse recordings, ensuring a balanced and varied representation of emotions across speakers and environments. The resulting database provides a comprehensive, high-quality resource, better suited for advancing SER systems in practical, real-world scenarios.