The human voice is very versatile and carries a multitude of emotions. Emotion in speech carries extra insight about human actions. Through further analysis, we can better understand the motives of people, whether they are unhappy customers or cheering fans. Humans are easily able to determine the emotion of a speaker, but the field of emotion recognition through machine learning is an open research area. We begin our study of emotion in speech by detecting one emotion. Specifically, we investigate the classification of happy, sad, anger or other related emotions in speech samples. In our analysis of emotion, we start by delineating the data used. We transition to discussing our methodology, and through this analysis, we investigate the best algorithms to select features that are relevant to predicting emotion. We also consider multiple machine learning models with which to classify emotion.
The human voice is very versatile and carries a multitude of emotions. Emotion in speech carries extra insight about human actions. Through further analysis, we can better understand the motives of people, whether they are unhappy customers or cheering fans. Humans are easily able to determine the emotion of a speaker, but the field of emotion recognition through machine learning is an open research area. We begin our study of emotion in speech by detecting one emotion. Specifically, we investigate the classification of happy, sad, anger or other related emotions in speech samples. In our analysis of emotion, we start by delineating the data used. We transition to discussing our methodology, and through this analysis, we investigate the best algorithms to select features that are relevant to predicting emotion. We also consider multiple machine learning models with which to classify emotion.
Using Vocals Determine Human Emotion
Using Vocals Determine Human Emotion
Product Details
| BN ID: | 2940179866824 |
|---|---|
| Publisher: | Faiz ul haque Zeya |
| Publication date: | 03/30/2025 |
| Sold by: | Draft2Digital |
| Format: | eBook |
| File size: | 819 KB |