In this study, we aim to identify moments of rudeness between two individuals. In particular, we segment all occurrences of rudeness in conversations into three broad, distinct categories and try to identify each. We show how machine learning algorithms can be used to identify rudeness based on acoustic and semantic signals extracted from conversations. Furthermore, we make note of our shortcomings in this task and highlight what makes this problem inherently difficult. Finally, we provide next steps which are needed to ensure further success in identifying rudeness in conversations.
Also, neural word embeddings are useful for identifying speech disfluencies (e.g., um", mid-sentence restarts, etc.) @cite_11 and can achieve an F1 score greater than 0.85 using a bi-directional LSTM neural network on the Switchboard corpus of telephone conversation transcripts. This approach is particularly interesting because conversational text is different from spoken language in that it does not contain speech disfluencies. In theory, any algorithm which aims to successfully identify rude conversational behaviour must be robust against speech disfluencies.
@cite_11
1986316091
Patients with Parkinsons Disease (PD) frequently exhibit deficits in the production of emotional speech. In this paper, we examine the classification of emotional speech in patients with PD and the classification of PD speech. Participants were recorded speaking short statements with different emotional prosody which were classified with three methods (naive Bayes, random forests, and support vector machines) using 209 unique auditory features. Feature sets were reduced using simple statistical testing. We achieve accuracies of 65.5 and 73.33 on classifying between the emotions and between PD vs. control, respectively. These results may assist in the future development of automated early detection systems for diagnosing patients with PD.