Global Single-Modal Affective Computing and Multimodal Affective Computing Market 2025 by Company, Regions, Type and Application, Forecast to 2031

According to our (Global Info Research) latest study, the global Single-Modal Affective Computing and Multimodal Affective Computing market size was valued at US$ 42190 million in 2024 and is forecast to a readjusted size of USD 126040 million by 2031 with a CAGR of 19.5% during review period.

Single-modal affective computing refers to the use of a single input modality (e.g., facial expressions, speech, text, or physiological signals) to recognize, analyze, and interpret human emotional states. These inputs are typically processed using specialized algorithms and sensors to enable emotion recognition capabilities. Common techniques in single-modal affective computing include speech emotion analysis, facial expression recognition, and text-based sentiment analysis.

Multimodal affective computing, on the other hand, involves the simultaneous use of multiple input modalities (such as speech, facial expressions, gestures, text) to recognize and interpret emotional states. This approach improves the accuracy and comprehensiveness of emotion recognition. Multimodal systems typically combine voice, facial expressions, and other sensory data to better capture emotional responses.

The market for both single-modal and multimodal affective computing is expanding rapidly, driven by several factors:

Technological Advancements: Advances in AI and machine learning are enhancing the accuracy and efficiency of emotion recognition algorithms, particularly in facial recognition, speech recognition, and other modalities.

Rising Demand for Consumer Experience: As personalized services and smart interactions become more popular, the application of affective computing in customer service, retail, and e-commerce is increasing. Emotion recognition helps businesses optimize user experiences and improve customer satisfaction.

Growing Need for Health Monitoring: In the context of mental health and aging populations, affective computing plays a crucial role in monitoring and assessing emotional well-being, providing more accurate health interventions.

Risks Facing the Market

Privacy Concerns: Emotion data, especially from facial recognition and voice emotion analysis, may raise privacy concerns, potentially leading to stricter regulations.

Technical Limitations: Multimodal systems may face challenges such as data synchronization and algorithm fusion issues, affecting the overall accuracy of emotion recognition.

Cultural and Contextual Differences: Emotional expression varies significantly across cultures and languages, which limits the universality of affective computing models, requiring localization and adjustment.

Market Concentration

The affective computing market is still in a rapid growth phase, with relatively low market concentration. While large tech companies (e.g., Google, Microsoft) have made strides in this field, many startups and mid-sized companies are also innovating with different emotion recognition solutions. Over the next few years, as technologies mature and industry standards are set, market concentration is likely to rise, with leading players dominating the field.

Downstream Demand Trends

Intelligent Customer Service and Virtual Assistants: With the rising demand for personalized customer experiences, the need for emotion recognition in intelligent customer service and virtual assistants continues to grow, particularly in industries such as e-commerce, finance, and healthcare.

Applications in Education and Healthcare: The education sector is beginning to leverage affective computing to enhance student learning experiences, while the healthcare industry focuses on using these technologies for mental health management and early diagnosis of psychiatric conditions.

Entertainment and Interactive Experiences: The gaming, virtual reality, and augmented reality industries are exploring how affective computing can enhance user engagement and interactivity, creating more immersive experiences.

This report is a detailed and comprehensive analysis for global Single-Modal Affective Computing and Multimodal Affective Computing market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.

Key Features:

Global Single-Modal Affective Computing and Multimodal Affective Computing market size and forecasts, in consumption value ($ Million), 2020-2031

Global Single-Modal Affective Computing and Multimodal Affective Computing market size and forecasts by region and country, in consumption value ($ Million), 2020-2031

Global Single-Modal Affective Computing and Multimodal Affective Computing market size and forecasts, by Type and by Application, in consumption value ($ Million), 2020-2031

Global Single-Modal Affective Computing and Multimodal Affective Computing market shares of main players, in revenue ($ Million), 2020-2025

The Primary Objectives in This Report Are:

To determine the size of the total market opportunity of global and key countries

To assess the growth potential for Single-Modal Affective Computing and Multimodal Affective Computing

To forecast future growth in each product and end-use market

To assess competitive factors affecting the marketplace

This report profiles key players in the global Single-Modal Affective Computing and Multimodal Affective Computing market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include Microsoft (Azure Cognitive Services - Emotion API), IBM (Watson Tone Analyzer), Google (DialogFlow - Emotion Detection), Sensum, Hewlett Packard Enterprise (HPE), Moodstocks (Acquired by Google), Clarifai, EmoTech, XOXCO (Fritz AI), Cogito (formerly Cogito Corp), etc.

This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.

Market segmentation

Single-Modal Affective Computing and Multimodal Affective Computing market is split by Type and by Application. For the period 2020-2031, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.

Market segment by Type
Single-Modal Affective Computing
Multimodal Affective Computing

Market segment by Application
Customer Service
Retail
Automotive
Healthcare
Education
Security
Entertainment
Others

Market segment by players, this report covers
Microsoft (Azure Cognitive Services - Emotion API)
IBM (Watson Tone Analyzer)
Google (DialogFlow - Emotion Detection)
Sensum
Hewlett Packard Enterprise (HPE)
Moodstocks (Acquired by Google)
Clarifai
EmoTech
XOXCO (Fritz AI)
Cogito (formerly Cogito Corp)
Affectiva (now part of Smart Eye)
Realeyes
Beyond Verbal
Emotient (Acquired by Apple)
Kairos
Nviso
Vocalis Health
iMotions
Affectum

Market segment by regions, regional analysis covers

North America (United States, Canada and Mexico)

Europe (Germany, France, UK, Russia, Italy and Rest of Europe)

Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)

South America (Brazil, Rest of South America)

Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)

The content of the study subjects, includes a total of 13 chapters:

Chapter 1, to describe Single-Modal Affective Computing and Multimodal Affective Computing product scope, market overview, market estimation caveats and base year.

Chapter 2, to profile the top players of Single-Modal Affective Computing and Multimodal Affective Computing, with revenue, gross margin, and global market share of Single-Modal Affective Computing and Multimodal Affective Computing from 2020 to 2025.

Chapter 3, the Single-Modal Affective Computing and Multimodal Affective Computing competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.

Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2020 to 2031

Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2020 to 2025.and Single-Modal Affective Computing and Multimodal Affective Computing market forecast, by regions, by Type and by Application, with consumption value, from 2026 to 2031.

Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.

Chapter 12, the key raw materials and key suppliers, and industry chain of Single-Modal Affective Computing and Multimodal Affective Computing.

Chapter 13, to describe Single-Modal Affective Computing and Multimodal Affective Computing research findings and conclusion.