Abstract:
"
In a competitive market world, organizations try to form good customer relationships to improve
their sales by improving customer satisfaction. Most of the conventional methods to measure
customer satisfaction are a hassle for the customer and do not always generate accurate results.
With the rapid development of technology, a new way to measure satisfaction is Speech Emotion
Recognition (SER) approach. Currently, it is both a challenging and emerging area. Even though
it is a growing area, there’s no research about speech emotion recognition in the Sinhala language.
Another issue is the lack of standard speech emotion data corpus for this area in the Sinhala
language.
To overcome this limitation, this study proposes an approach to detect emotions embedded in
Sinhala language speech and then the detected emotions are then mapped to a satisfaction status.
The main emotion classes used in this study are happiness, anger, sadness, and emotionless state
plus considering speaker of the gender. The speech data corpus is formed using Sinhala wide
screen movie speech data. This study uses CNN (1D) classification to perform the emotion
classification task. The analysis results show that the model’s overall accuracy to identify the
emotion along with gender is 62.58% and it resulted in 56% of precision, 63% of recall, and 57%
of F1 score."
