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This study proposes the novel formulation of measuring emotional similarity between speech recordings. This formulation explores the ordinal nature of emotions by comparing emotional similarities instead of predicting an emotional attribute, or recognizing an emotional category. The proposed task determines which of two alternative samples has the most similar emotional content to the emotion of a given anchor. This task raises some interesting questions. Which is the emotional descriptor that provide the most suitable space to assess emotional similarities? Can deep neural networks (DNNs) learn representations to robustly quantify emotional similarities? We address these questions by exploring alternative emotional spaces created with attribute-based descriptors and categorical emotions. We create the representation using a DNN trained with the triplet loss function, which relies on triplets formed with an anchor, a positive example, and a negative example. We select a positive sample that has similar emotion content to the anchor, and a negative sample that has dissimilar emotion to the anchor. The task of our DNN is to identify the positive sample. The experimental evaluations demonstrate that we can learn a meaningful embedding to assess emotional similarities, achieving higher performance than human evaluators asked to complete the same task. 

Human-computer interactions can be very effective, especially if computers can automatically recognize the emotional state of the user. A key barrier for effective speech emotion recognition systems is the lack of large corpora annotated with emotional labels that reflect the temporal complexity of expressive behaviors, especially during multiparty interactions. This pa- per introduces the MSP-Conversation corpus, which contains interactions annotated with time-continuous emotional traces for arousal (calm to active), valence (negative to positive), and dominance (weak to strong). Time-continuous annotations offer the flexibility to explore emotional displays at different temporal resolutions while leveraging contextual information. This is an ongoing effort, where the corpus currently contains more than 15 hours of speech annotated by at least five annotators. The data is sourced from the MSP-Podcast corpus, which contains speech data from online audio-sharing websites annotated with sentence-level emotional scores. This data collection scheme is an easy, affordable, and scalable approach to obtain natural data with diverse emotional content from multiple speakers. This study describes the key features of the corpus. It also compares the time-continuous evaluations from the MSP- Conversation corpus with the sentence-level annotations of the MSP-Podcast corpus for the speech segments that overlap between the two corpora. 

The ability to identify speech with similar emotional content is valuable to many applications, including speech retrieval, surveillance, and emotional speech synthesis. While current formulations in speech emotion recognition based on classification or regression are not appropriate for this task, solutions based on preference learning offer appealing approaches for this task. This paper aims to find speech samples that are emotionally similar to an anchor speech sample provided as a query. This novel formulation opens interesting research questions. How well can a machine complete this task? How does the accuracy of automatic algorithms compare to the performance of a human performing this task? This study addresses these questions by training a deep learning model using a triplet loss function, mapping the acoustic features into an embedding that is discriminative for this task. The network receives an anchor speech sample and two competing speech samples, and the task is to determine which of the candidate speech sample conveys the closest emotional content to the emotion conveyed by the anchor. By comparing the results from our model with human perceptual evaluations, this study demonstrates that the proposed approach has performance very close to human performance in retrieving samples with similar emotional content.