Perception and prediction of speaker appeal – A single speaker study. (November 2018)

• Record Type: Journal Article
• Authors: Cullen, Ailbhe; Hines, Andrew; Harte, Naomi
• Is Part Of: Computer speech & language. Volume 52(2018)
• Page Start: 23
• Page End: 40
• Publication Date: 2018-11
• Subjects: Computational paralinguistics -- Speaker trait detection -- Speaker appeal -- Political speech -- Speech processing; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2018.04.004
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Prediction of speaker appeal from real world recordings of political speech. Comparison of noise-robust acoustic features for prediction of speaker appeal. Demonstration of annotator bias according to the situation of the source recording. This bias is exploited to improve prediction by using situation specific models. Longitudinal analysis of speaker appeal in one speaker over a 7 year period. Abstract: In this paper we explore the automatic prediction of speaker appeal from recordings of political speech. The database used contains recordings of a single speaker in a wide range of situations (interview, election rally etc.) which has been annotated for six speaker traits: boring; charismatic; enthusiastic; inspiring; likeable; and persuasive. The aim of this study is to predict these ratings using acoustic features of the speech. We offer three key contributions in this paper. Firstly, we explore the effect of acoustic environment on the perception of speaker ability. We find significant biases in the perception of all six traits, with interview speech being consistently rated as less appealing, and election rally speech as more appealing. In our second contribution, we attempt to exploit this bias by modelling speech from each situation separately, which gives a significant improvement in classification performance. Finally, the database covers 7 years. Thus, our third contribution is an analysis of the variance in both annotations and acoustic features over time to uncover temporal trends in speaker appeal. We find significant trends which show a decline in the speaker's prosodic activity over time, which mirror a decline in the perception of speaker appeal as measured by the database annotations.