Building Naturalistic Emotionally Balanced Speech Corpus by Retrieving Emotional Speech from Existing Podcast Recordings

By Reza Lotfian, Carlos Busso. IEEE Transactions on Affective Computing, 2019

467 citations recorded by the source corpus.

The lack of a large, natural emotional database is one of the key barriers to translate results on speech emotion recognition in controlled conditions into real-life applications. Collecting emotional databases is expensive and time demanding, which limits the size of existing corpora. Current approaches used to collect spontaneous databases tend to provide unbalanced emotional content, which is dictated by the given recording protocol (e.g., positive for colloquial conversations, negative for discussion or debates). The size and speaker diversity are also limited. This paper proposes a novel approach to effectively build a large, naturalistic emotional database with balanced emotional content, reduced cost and reduced manual labor. It relies on existing spontaneous recordings obtained from audio-sharing websites. The proposed approach combines machine learning algorithms to retrieve recordings conveying balanced emotional content with a cost effective annotation process using crowdsourcing, which make it possible to build a large scale speech emotional database. This approach provides natural emotional renditions from multiple speakers, with different channel conditions and conveying balanced emotional content that are difficult to obtain with alternative data collection protocols.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ac854a011876d06dc3eeb8b03dfc26ad129a7474

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Citations in the corpus

Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings (2021, methodology): “With the exception of MSP-Podcast, these datasets are relatively small in size, usually including only a few dozen speakers.”
A Survey of Deep Active Learning (2020, methodology): “[85, 171] MIT-BIH [142], INCART, SVDB [171] Electrocardiogram (ECG) signal classification [1] MSP-Podcast [138] Speech emotion recognition 1http://lila.”
Pengi: An Audio Language Model for Audio Tasks (2023, background): “[63], MELD [48]; Music Analysis: NSynth [19], FMA [9]; Audio Captioning: AudioCaps [32], ClothoV2 [15]; Audio Question and Answering: ClothoAQA [39]; Auxiliary: WavText5K [12], SoundDescs [35], MACS [42], WavCaps [43], FreeSound [20] and FindSound 2 .”