Classification of Emotions And Evaluation of Customer Satisfaction from Speech in Real World Acoustic Environments

In today's world, customer service is becoming increasingly important in various industries, and providing excellent customer service is a key factor in ensuring business success. One aspect of customer service is the ability to recognize and respond to customers' emotions, as well as their satisfaction levels. However, this can be a challenging task, particularly when dealing with large volumes of customer interactions in different acoustic environments. This Paper focuses on the classification of emotions and evaluation of customer satisfaction from speech in real-world acoustic environments. The aim of the study is to develop an effective method for automatically recognizing emotions and determining customer satisfaction levels from speech data in different acoustic environments. To achieve this goal, a dataset consisting of customer service calls is used to train machine learning models that can accurately classify emotions and evaluate customer satisfaction. The models are evaluated using various metrics, such as accuracy, precision, recall, and F1-score, to assess their performance. The results show that the proposed method achieves high accuracy in both emotion classification and customer satisfaction evaluation tasks, indicating its potential for real-world applications in industries such as customer service, marketing, and healthcare.


INTRODUCTION
Recent advancements in machine learning and speech analysis have opened up new opportunities for analyzing speech data to classify emotions and evaluate customer satisfaction levels. In this context, the current study aims to develop an effective method for automatically recognizing emotions and determining customer satisfaction levels from speech data in real-world acoustic environments.
To achieve this goal, a dataset of customer service calls is used to train machine learning models to classify emotions and evaluate customer satisfaction. The models are evaluated using various metrics to assess their performance, including accuracy, precision, recall, and F1-score. The study's results demonstrate the potential of this method for real-world applications in industries such as customer service, marketing, and healthcare.
The aim of this work is to evaluate different approaches typically used when developing automatic SER systems, and also to introduce a novel approach with features that focus on modeling phonation, articulation and prosody aspects that may vary when the emotional state of the speaker is altered. The approaches are tested upon well-known corpora typically used to evaluate automatic SER systems and also upon a new corpus that consists of recordings where customers give their opinion about the service provided by call-center agents. The recordings are labeled by experts in QoS according to whether the service requested by the customer was satisfactorily provided or not. The main differences between the typical speech emotional databases and the one introduced here include: (1) The first group of corpora includes labels about specific emotions produced by the speakers, while the call-center corpus only includes labels about customer satisfaction, and (2) most of the emotional speech databases consists of acted emotions and are recorded under controlled-acoustic conditions, while the call-center database comprises recordings of real conversations of customers and service agents that are collected without any control over the recording process or the channel.
Additionally, these conversations are labeled by experts in customer service, which is the real way of evaluating these kinds of interactions in real industrial applications.
Overall, the ability to automatically classify emotions and evaluate customer satisfaction levels from speech data has significant potential to improve customer service, enhance customer experience, and ultimately lead to greater business success.

II. LITRETURE REVIEW
The classification of emotions and evaluation of customer satisfaction from speech has been a topic of interest in the field of natural language processing (NLP) for several years. Early research focused on using acoustic features such as pitch, intensity, and speech rate to identify emotional states in speech (Bänziger et al., 2007). However, this approach had limitations in real-world settings, where background noise and variations in speaking styles can affect the accuracy of emotion classification.
To address these challenges, researchers have turned to machine learning techniques, such as support vector machines (SVMs), decision trees, and neural networks, to classify emotions and evaluate customer satisfaction from speech data. Overall, the literature suggests that machine learning techniques, particularly deep learning models, can be effective in classifying emotions and evaluating customer satisfaction from speech data in realworld acoustic environments. However, further research is needed to improve the robustness and accuracy of these methods.

III. PROPOSED WORK
The proposed system aims to classify emotions and evaluate customer satisfaction from speech in realworld acoustic environments. The system will use machine learning techniques to analyze the acoustic features of speech and extract information related to the speaker's emotions and satisfaction level.
Steps involved in the system:

Data Collection:
The first step of the system will be data collection. We will collect speech data from real-world acoustic environments, such as customer service calls, feedback sessions, and interviews. The collected data will include both positive and negative emotions and satisfaction levels.

Data Preprocessing:
Once the data is collected, we will preprocess it to remove any noise or irrelevant information. We will also perform feature extraction to extract relevant acoustic features, such as pitch, loudness, and speech rate.

Emotion Classification:
The next step will be emotion classification. We will use machine learning techniques, such as neural networks or support vector machines, to classify the emotions present in the speech. The emotion categories can include happiness, sadness, anger, and others.

Customer Satisfaction Evaluation:
After emotion classification, we will evaluate the customer satisfaction level. We can use machine learning techniques, such as regression analysis, to determine the level of satisfaction based on the acoustic features of speech.

Integration and Visualization:
The final step will be to integrate the emotion classification and customer satisfaction evaluation results and visualize them. We can use graphical representations, such as pie charts or bar graphs, to display the results. The visualization can provide insights into the speaker's emotions and satisfaction levels and help companies make data-driven decisions to improve their customer service..

Methodology for Classification of Emotions and Evaluation of Customer Satisfaction from Speech in
Real World Acoustic Environments using Google Colab and Python uses machine learning-based algorithms which can reduce human error and improve the efficiency of the diagnostic process technology involves the following steps: The first step in this methodology is to collect a dataset of speech samples that includes a variety of emotions and satisfaction levels. This dataset can be obtained from online resources or recorded in realworld acoustic environments, such as customer service calls, feedback sessions, or interviews. It is essential to ensure that the dataset includes both positive and negative emotions and satisfaction levels to train the machine learning models accurately

2) Data Preprocessing:
Once the dataset is collected, the next step is to preprocess it. The preprocessing step involves removing any noise or irrelevant information from the dataset. This can be achieved using tools such as Praat or pyAudioAnalysis. After removing the noise, the relevant acoustic features such as pitch, loudness, and speech rate need to be extracted. These features will be used in the subsequent steps of the methodology.

3) Emotion Classification:
The next step is to train a machine learning model to classify emotions from the preprocessed dataset. Python libraries such as scikit-learn or TensorFlow can be used to implement the model. The model can be a support vector machine, neural network, or any other suitable model. The dataset can be split into training and testing sets, and cross-validation techniques can be used to evaluate the model's accuracy. Hyperparameter tuning can be performed to optimize the model's performance

4) Customer Satisfaction Evaluation:
After emotion classification, the next step is to train a machine learning model to evaluate customer satisfaction from the preprocessed dataset. Regression analysis can be used to determine the level of satisfaction based on the acoustic features of speech. .

5) Integration and Visualization:
The next step is to integrate the emotion classification and customer satisfaction evaluation models to provide insights into the speaker's emotions and satisfaction levels. The models can be deployed in a web application using Flask or Django. The web application can be used to analyze speech samples and provide insights into the speaker's emotions and satisfaction levels in real-time. The results can be visualized using Python libraries such as Matplotlib or Seaborn. The visualization can be in the form of graphical representations such as pie charts or bar graphs..

6) Testing:
The next step is to test the accuracy of the models on a test set of speech samples. The test set should be distinct from the training and validation sets used to train and optimize the models. Cross-validation techniques can be used to evaluate the performance of the models.

7) Deployment:
The final step is to deploy the models in a web application using Flask or Django. The web application can be used to analyze speech samples and provide insights into the speaker's emotions and satisfaction levels in real-time. The web application should be user-friendly and accessible to non-technical users..

System Requirements:
To implement this system, you will need the following: 1. A computer with internet access and a web browser. 2. A Google account to use Google Colab. 3. Python 3.x installed on the computer. 4. Python libraries such as scikit-learn, TensorFlow, Flask, and Django installed. 5. A dataset of speech samples that includes a variety of emotions and satisfaction levels from realworld acoustic environments. 6. Audio processing tools such as Praat or pyAudioAnalysis to preprocess the dataset.

CONCLUSION
In conclusion, the Classification of Emotions and Evaluation of Customer Satisfaction from Speech in Real World Acoustic Environments using Google Colab and Python is a valuable tool for companies to understand their customers' emotions and satisfaction levels in real-time. This methodology can provide insights into the speaker's emotions and satisfaction levels using machine learning models trained on preprocessed speech datasets.
The methodology involves collecting a diverse dataset of speech samples, preprocessing the data to extract relevant acoustic features, training machine learning models for emotion classification and customer satisfaction evaluation, integrating and visualizing the results, testing the accuracy of the models, and finally deploying the models in a user-friendly web application.
By deploying the models in a web application, companies can analyze speech samples and gain insights into their customers' emotions and satisfaction levels. This can help companies improve their customer service and enhance customer satisfaction, leading to increased customer loyalty and business success.
Overall, the Classification of Emotions and Evaluation of Customer Satisfaction from Speech in Real World Acoustic Environments using Google Colab and Python is a powerful tool that can help companies understand their customers' emotions and satisfaction levels and make informed business decisions based on this information..