Multimodal Analysis of Acoustic and Linguistic Features in Entrepreneurial Pitches using Deep Learning
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Önal Ertugrul, I. | |
| dc.contributor.author | Aken, Pepijn van | |
| dc.date.accessioned | 2023-02-03T01:00:57Z | |
| dc.date.available | 2023-02-03T01:00:57Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/43488 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | This study explores the impact of behavioural cues on decisions that involve investing in a startup on the basis of a pitch. A multimodal approach is developed in which acoustic and linguistic features are extracted from recordings of entrepreneurial pitches to predict the likelihood of investment. The acoustic and linguistic modalities are represented using both hand-crafted and deep features. The capabilities of deep learning models are exploited to capture the temporal dynamics of the inputs. | |
| dc.title | Multimodal Analysis of Acoustic and Linguistic Features in Entrepreneurial Pitches using Deep Learning | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Artificial Intelligence | |
| dc.thesis.id | 13465 |