The MSP-Podcast Corpus
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909783330652160 |
|---|---|
| author | Busso, Carlos Lotfian, Reza Sridhar, Kusha Salman, Ali N. Lin, Wei-Cheng Goncalves, Lucas Parthasarathy, Srinivas Naini, Abinay Reddy Leem, Seong-Gyun Martinez-Lucas, Luz Chou, Huang-Cheng Mote, Pravin |
| author_facet | Busso, Carlos Lotfian, Reza Sridhar, Kusha Salman, Ali N. Lin, Wei-Cheng Goncalves, Lucas Parthasarathy, Srinivas Naini, Abinay Reddy Leem, Seong-Gyun Martinez-Lucas, Luz Chou, Huang-Cheng Mote, Pravin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09791 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The MSP-Podcast Corpus Busso, Carlos Lotfian, Reza Sridhar, Kusha Salman, Ali N. Lin, Wei-Cheng Goncalves, Lucas Parthasarathy, Srinivas Naini, Abinay Reddy Leem, Seong-Gyun Martinez-Lucas, Luz Chou, Huang-Cheng Mote, Pravin Audio and Speech Processing Sound 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. |
| title | The MSP-Podcast Corpus |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2509.09791 |