Abstract
Entrepreneurial pitch competitions have become increasingly
popular in the start-up culture to attract prospective investors. As the
ultimate funding decision often follows from some form of social interaction,
it is important to understand how the decision-making process
of investors is influenced by behavioral cues. In this work, we examine
whether vocal features are associated with the ultimate funding decision
of investors by utilizing deep learning methods.We used videos of individuals
in an entrepreneurial pitch competition as input to predict whether
investors will invest in the startup or not. We proposed models that combine
deep audio features and Handcrafted audio Features (HaF) and feed
them into two types of Recurrent Neural Networks (RNN), namely Long
Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). We
also trained the RNNs with only deep features to assess whether HaF
provide additional information to the models. Our results show that it is
promising to use vocal behavior of pitchers to predict whether investors
will invest in their business idea. Different types of RNNs yielded similar
performance, yet the addition of HaF improved the performance.
popular in the start-up culture to attract prospective investors. As the
ultimate funding decision often follows from some form of social interaction,
it is important to understand how the decision-making process
of investors is influenced by behavioral cues. In this work, we examine
whether vocal features are associated with the ultimate funding decision
of investors by utilizing deep learning methods.We used videos of individuals
in an entrepreneurial pitch competition as input to predict whether
investors will invest in the startup or not. We proposed models that combine
deep audio features and Handcrafted audio Features (HaF) and feed
them into two types of Recurrent Neural Networks (RNN), namely Long
Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). We
also trained the RNNs with only deep features to assess whether HaF
provide additional information to the models. Our results show that it is
promising to use vocal behavior of pitchers to predict whether investors
will invest in their business idea. Different types of RNNs yielded similar
performance, yet the addition of HaF improved the performance.
| Original language | English |
|---|---|
| Pages | 1-14 |
| Number of pages | 14 |
| Publication status | Published - Aug 2022 |
| Event | 12th international workshop on human behavior understanding - Montreal, Canada Duration: 21 Aug 2022 → 21 Aug 2022 Conference number: 12 https://www.cmpe.boun.edu.tr/hbu/2022/index.html |
Conference
| Conference | 12th international workshop on human behavior understanding |
|---|---|
| Abbreviated title | HBU |
| Country/Territory | Canada |
| City | Montreal |
| Period | 21/08/22 → 21/08/22 |
| Internet address |
Keywords
- Vocal behavior
- Entrepreneurial decision making
- Deep learning
- VGGish
- LSTM
- GRU