Description:FIELD OF INVENTION
AI, sentiment analysis, dialect speech processing, natural language processing (NLP), machine learning, deep learning, speech recognition, affective computing, human-computer interaction, computational linguistics, speech emotion recognition, voice analysis, feature extraction, neural networks, data mining, real-time analytics, psychological AI, emotion AI, speech-to-text, language modeling, cognitive computing.
BACKGROUND OF INVENTION
Emotion analysis, a subfield of natural language processing (NLP) and AI, focuses on extracting and interpreting emotions from text, speech, and other forms of data. The foundation of this field can be traced back to early 20th-century psychology, particularly Basic Emotion Theory, proposed by William James and later expanded by Paul Ekman. This theory suggests the existence of universal emotions, such as happiness, sadness, anger, fear, disgust, and surprise, which are recognizable across different cultures.
During the 1990s, affective computing research advanced with the development of computational technologies and the availability of large datasets. Researchers began creating algorithms to automatically analyze and classify emotions in text data. Early methods relied on lexicons and linguistic features to categorize emotions into discrete groups. With the rise of deep learning, more sophisticated techniques emerged, including Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). These models enhanced contextual understanding and improved the accuracy of emotion classification.
Existing Methodology
Current methods use Machine Learning (ML) and Deep Learning (DL) techniques, such as Mel-Frequency Cepstral Coefficients (MFCCs), Spectrograms, and Prosodic features, for speech emotion detection. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformers are widely used for classifying emotions. However, these models struggle with dialectal diversity, code-switching, and low-resource languages. The invention aims to bridge this gap by integrating dialect-specific AI models with real-time emotion detection.
The patent application number 201921052179 discloses a system and method to quantify subject-specific sentiment. A system using ai and nlp to analyze and quantify sentiment based on individual linguistic patterns, emotions, and contextual speech data.
The patent application number 201947052343 discloses a device, system and method for determining an emotional state of a user. A device, system, and method analyze speech, tone, and expressions using ai to determine a user's emotional state in real time.
The patent application number 202041001962 discloses a method and a system for controlling emotional state of a user. A system using ai and biofeedback to analyze emotions and adjust stimuli, improving user well-being through real-time emotional regulation.
The patent application number 202021046176 discloses a selection of speech segments for training classifiers for detecting emotional valence from input speech signals. Selecting key speech segments enhances classifier training by extracting emotional cues like pitch, tone, and prosody, improving valence detection accuracy.
The patent application number 202041048725 discloses a system for sentiment analysis and a method thereof. A system using ai and nlp for sentiment analysis, processing speech/text data to detect emotions, opinions, and sentiments in real time.
SUMMARY
This invention presents an AI-based equipment for emotional sentiment analysis of dialect speech, designed to recognize and interpret emotions from spoken dialects in real time. The system integrates natural language processing (NLP), speech emotion recognition (SER), and deep learning (DL) to analyze vocal expressions, tone, pitch, and speech patterns. Unlike conventional sentiment analysis tools, which focus on standardized languages, this innovation is tailored for dialect-specific speech variations, enabling more accurate emotion detection in diverse linguistic settings.
Objective
The primary objective is to develop an intelligent speech-based sentiment analysis system that can:
1. Accurately classify emotions in various dialects.
2. Improve human-computer interaction (HCI) through emotion-aware AI.
3. Support applications in healthcare, customer service, automotive AI, and social robotics.
4. Enhance real-time speech processing using Edge AI for on-device analysis.
This invention aims to bridge linguistic diversity in AI-driven sentiment analysis.
DETAILED DESCRIPTION OF INVENTION
Emotion Analysis Using Artificial Intelligence
Emotion analysis, also known as sentiment analysis or affective computing, aims to identify and interpret human emotions using various cues such as facial expressions, body language, speech patterns, and physiological signals. This field integrates psychology, computer science, and artificial intelligence (AI) to develop algorithms capable of accurately recognizing and understanding human emotions. The goal is to enable machines to comprehend and respond to emotions naturally and empathetically, with applications in customer service, healthcare, education, and entertainment.
Emotion Analysis and Recognition Systems
Emotion analysis and recognition systems utilize AI-based algorithms to interpret human emotions from multiple input sources, such as text, speech, facial expressions, and physiological signals. These systems incorporate NLP, machine learning, computer vision, and signal processing to classify emotions accurately. They are widely used in customer feedback analysis, mental health assessment, market research, and human-computer interaction systems.
Process of Emotion Analysis and Recognition
1. Data Collection and Preprocessing
o Converts speech signals to text
o Extracts facial features from images
o Filters noise and refines input data
2. Feature Extraction
o Analyzes sentiment, tone, and facial expressions
o Identifies emotion-related patterns in the data
3. Machine Learning Model Training
o Uses labeled datasets to train AI models
o Implements rule-based systems or deep learning architectures
4. Emotion Prediction and Classification
o Recognizes emotional patterns in real-time
o Categorizes emotions using trained models
5. Evaluation and Validation
o Measures performance using accuracy, precision, and recall
o Conducts user feedback assessments for subjective validation
By leveraging AI and deep learning, emotion analysis systems continue to evolve, improving their ability to enhance human-computer interaction and provide valuable insights across various domains.

Figure 1: Analysing Method
Types of Emotion Analysis in AI
Emotion AI, also known as affective computing, is categorized into four main types:
1. Facial Expression Analysis
o Detects emotions by analyzing facial features and movements.
o Uses facial recognition algorithms to identify emotions such as happiness, sadness, anger, surprise, and fear.
2. Speech Emotion Recognition
o Evaluates voice patterns, including tone, pitch, speech rate, and word choice, to infer emotional states.
o Utilizes technologies like audio signal processing (ASP), machine learning (ML), and natural language processing (NLP).
3. Physiological Signal Analysis
o Monitors physiological responses such as heart rate, galvanic skin response, breathing rate, and brain activity (via EEGs).
o Employs wearable devices and sensors to collect data, which machine learning algorithms use to interpret emotions.

4. Multimodal Approaches
o Combines multiple data sources, such as facial expressions, speech, and physiological signals, for a more comprehensive emotion analysis.
o Integrates different sensor technologies and algorithms to improve accuracy and reliability.
Perspectives on Emotion AI
Emotion analysis through AI is examined from various perspectives:
• Psychological Perspective
o Helps in understanding human emotions to improve mental well-being.
o AI can analyze speech, text, facial expressions, and physiological signals to detect emotions, assisting psychologists in developing personalized interventions.
• Social Perspective
o Provides insights into public sentiment by analyzing social media posts, online reviews, and customer feedback.
o Governments, businesses, and organizations can leverage this data to make informed decisions and enhance strategies.
• Human-Computer Interaction Perspective
o Enables AI-powered systems to recognize and respond to human emotions in real time.
o Virtual assistants can adjust their tone and responses based on user emotions, leading to more engaging and empathetic interactions.
Applications of Emotion AI
Emotion AI is transforming various industries by enhancing decision-making and user experiences:
• Marketing & Advertising
o Helps brands understand emotional triggers influencing purchasing decisions.
o A Grand View Research report estimates the emotion analysis market in marketing will reach $3.8 billion by 2025.
• Healthcare
o Assists in detecting mental health conditions like depression and anxiety by analyzing speech patterns.
o A study in the Journal of Biomedical Informatics suggests emotion analysis can aid in early intervention.
• Customer Service
o Improves interactions by analyzing customer emotions in real time.
o According to Business Wire, emotion AI can enhance customer satisfaction and loyalty by enabling agents to adapt communication styles effectively.
• Education
o Assesses student engagement and emotional states during learning.
o Harvard researchers found that analyzing facial expressions and gestures helps tailor teaching methods for personalized learning experiences.
• Market Research
o Provides deeper insights into consumer preferences and opinions.
o Market Research Future reports that emotion AI helps companies improve product development and market competitiveness.
• Social Media Monitoring
o Analyzes emotions expressed in social media posts and comments to gauge public sentiment.
o Forbes reports that as of 2023, 4.9 billion people use social media, making it a valuable source for emotion analysis in brand perception and marketing strategies.
Emotion AI continues to advance, offering innovative solutions across industries by enhancing human understanding, decision-making, and interaction.
Emotion AI: Applications, Challenges, and Future Prospects
Applications of Emotion AI
Emotion AI, also known as affective computing, has a wide range of applications across various industries:
• Fraud Detection: By analyzing emotions in written or spoken claims, Emotion AI can help detect fraudulent activities such as insurance fraud by identifying inconsistencies or deceptive behavior.
• Virtual Reality & Gaming: It enhances user experience in gaming and virtual reality by dynamically adjusting difficulty levels, intensity, or storylines based on the user's emotional state, creating a more immersive experience.
• Virtual Assistants & Chatbots: By recognizing user emotions, virtual assistants and chatbots can provide more empathetic and personalized responses, improving user engagement and satisfaction.
• Automotive Industry: Emotion AI can be integrated into vehicles to monitor drivers' emotional and mental states in real time. It helps detect drowsiness, stress, or distraction, triggering alerts or taking preventive actions to enhance road safety.
Challenges in Emotion AI
Despite its potential, Emotion AI faces several key challenges:
• Subjectivity of Emotions: Emotions vary significantly among individuals, making it difficult to develop models that accurately analyze emotions across diverse populations.
• Context Dependence: Understanding emotions requires considering the context in which they are expressed. Detecting sarcasm, irony, or nuanced emotions is challenging without contextual awareness.
• Multimodal Data Integration: Emotions are conveyed through text, facial expressions, voice tone, and body language. Integrating these modalities for accurate emotion detection requires advanced techniques.
• Cultural & Linguistic Differences: Emotional expressions differ across cultures and languages, making it difficult to develop universal models that work across diverse populations.
• Lack of Labeled Data: Training effective models requires large datasets with accurately labeled emotions, but annotating emotions is complex due to their subjective nature.
• Emotional Ambiguity: Many emotions are mixed or unclear, making it difficult to categorize them into distinct labels.
• Real-time Processing: Emotion analysis in live scenarios, such as customer interactions, requires efficient and high-speed processing. Managing large volumes of data in real-time remains a challenge.
• Ethical & Privacy Concerns: The analysis of personal emotions raises concerns regarding user privacy, consent, and data security. Ensuring ethical use and data protection is critical.
• Bias in Emotion AI Models: AI models can develop biases due to imbalanced training datasets, potentially leading to unfair or inaccurate emotion analysis. Mitigating bias remains an ongoing challenge.
• Limited Understanding of Emotions: Despite advancements, emotions remain complex and not fully understood, making accurate AI-driven emotion recognition an ongoing area of research.
Future of Emotion AI
The future of Emotion AI looks promising with continuous advancements in technology and research:
• Improved Accuracy: Emotion recognition algorithms will become more precise, enabling better analysis and applications in various domains.
• Real-time Emotion Analysis: AI-driven emotion detection will provide instant feedback, benefiting areas like mental health monitoring, customer service, and human-computer interaction.
• Multimodal Emotion Analysis: Future AI models will integrate multiple data sources, including facial expressions, voice, body language, and physiological signals, for a more comprehensive understanding of emotions.
• Personalized Emotion Analysis: Advanced machine learning techniques will allow AI systems to recognize an individual’s unique emotional patterns and offer tailored recommendations or interventions.
Conclusion
Emotion AI has the potential to significantly enhance human-computer interaction by enabling machines to understand and respond to human emotions. However, challenges related to privacy, ethics, bias, and contextual understanding must be carefully addressed. Ensuring responsible development and deployment of these technologies is crucial. With ongoing research and advancements in data-driven approaches, the future of Emotion AI holds great promise for more accurate and meaningful emotion recognition and analysis.

DETAILED DESCRIPTION OF DIAGRAM
Figure 1: Analysing Method , Claims:1. An AI based Equipment for Emotional Sentiment Analysis for Dialect Speech claims that the AI-based equipment analyzes emotional sentiment from dialect speech using advanced machine learning techniques.
2. It accurately detects and classifies emotions such as happiness, sadness, anger, and neutrality from spoken dialects.
3. The system utilizes multimodal analysis, incorporating voice tone, pitch, and speech patterns for precise sentiment recognition.
4. It supports real-time emotion detection, enabling instant feedback in various applications like customer service and healthcare.
5. The model is trained on diverse dialect datasets to ensure high accuracy and adaptability across different linguistic variations.
6. The equipment integrates with virtual assistants, call centers, and surveillance systems for enhanced human-computer interaction.
7. It employs deep learning algorithms to minimize biases and improve sentiment detection across cultures and demographics.
8. The system ensures data privacy and ethical considerations by adhering to strict security protocols for speech data handling.
9. It is designed for scalability, allowing seamless integration with various speech recognition and natural language processing platforms.
10. The equipment enhances communication analysis, making it valuable for applications in education, mental health, and market research.