Emotional AI and affective computing allow your device to detect and react in real-time to nonverbal emotional cues.
The emotion detection and recognition (EDR) markets were valued at ~$20M in 2020, and they’re expected to reach ~53M by 2026, according to Mordor Intelligence.
The emotional AI market continues to grow due to, among other factors, the rising demand for virtual assistants to detect fraudulent activity and the growing need for improved security in various industries.
Emotional AI is based on affective computing, which allows your device to detect and react in real-time to nonverbal emotional cues.
If you’ve ever seen the Pixar movie ‘Inside Out’ you’ll remember the animated depictions of core emotions like Joy, Sadness, Anger, Fear, and Disgust. Emotional AI learns how to read those cues and respond accordingly.
We’re going to dive deeper into what we mean by emotional AI and examine some use cases, as well as touch on the amount of data you need to develop it.
How are we feeling so far?
What is emotional AI?
Affective computing, also known as emotion AI, automatically recognizes emotions.
A recent paper from AIMultiple defines emotional AI as “an emerging technology that enables computers and systems to identify, process, and simulate human feelings and emotions.”
It comes from machine learning systems that recognize, interpret, process, and simulate human feelings and emotions.
By now, we all make use of speech recognition technology. We talk into our phones and smart speakers and expect a valid response.
Play a song. Give me directions to the nearest gas station. What’s the weather?
Emotional AI takes this to another level.
It comes from the theory of “basic emotions” that states people everywhere communicate six basic internal emotional states using the same facial movements based on our biological and evolutionary origins.
Those emotions are happiness, surprise, fear, disgust, anger, and sadness, all of which we can read to certain degrees using facial expressions and body movements.
An AI emotion application generally includes the following steps:
- Acquire the image frame from a camera feed (IP, CCTV, USB camera).
- Preprocess the image (cropping, resizing, rotating, color correction).
- Extract the important features with Convolutional Neural Networks (CNNs)
- Perform emotion classification.
But effective detection, processing, classification can’t happen unless the AI is first trained to recognize these feelings.
How to Build Emotional AI
The simple answer is high-quality video, image, audio, and text data, and lots of it.
You need it all to read people’s emotions via tone of voice, facial expressions, gestures, body language and even text.
It must be annotated, because humans are still at top of the pyramid when it comes to recognizing these emotions – most of us, at least.
At its peak though, emotional AI can listen to us speak and detect voice inflections that correspond to anger or stress, for example.
Image and Video Data
This is best exemplified in the realm of facial recognition. Machine learning programs analyzes image data and looks for a very specific set of markers within it.
For emotional AI, it looks for facial expressions that help detect if you’re stressed, nervous, confident, satisfied etc.
It creates a database of facial markers and an image of a face that shares a critical threshold of similarity from database indicates a possible match.
The success of any facial recognition technology depends on the quantity and quality of image data used to train it.
As it stands, emotional AI remains controversial because researchers argue facial expressions vary widely between contexts and cultures. The technology is also seen as a breeding ground for cultural stereotypes.
Many examples are needed, and for each emotion, a large variety of images are required to build a robust understanding of the face.
And it all requires human image annotation to get the best possible and most accurate readings of your image data.
Emotional AI also analyzes a person’s speech through various factors to determine their sentiment:
- Tone of voice
- Elongated pauses
This technology can even detect hidden anxiety or tension when someone uses sarcasm, hyperbole, or says the opposite of what they mean.
Conversational data trains AI to replicate the flow of human conversations. It’s not just programming different languages and dialects, but also phraseology, pronunciations, filler words, slang, and other variables.
Call center data is a prime source, and you can also request voice actors to perform recorded speech patterns to replicate the desired emotions.
Again, the data must be comprehensive and representative of culture, age, dialect, and a wide variety of other demographic variables.
Deciphering emotion from text data is also known as sentiment analysis.
Natural language processing (NLP) and machine learning algorithms make sense of data through text classification.
Sentiment analysis then determines whether annotated text data is positive, negative, or neutral.
Emotion detection is a specific type of sentiment analysis. It identifies emotions people are expressing in their feedback, from happy and satisfied to angry and frustrated.
Practically, it helps businesses monitor brand and product customer feedback to better understand customer needs.
Online customer reviews are a gold mine. Google’s 5-star review system offers a base opportunity to detect customer sentiment:
- 5 stars = Very positive
- 4 stars = Positive
- 3 stars = Neutral
- 2 stars = Negative
- 1 star = Very negative
A detailed review offers much more to go on, though. However, sometimes emotional statements can be misleading when they come in written form.
Let’s say someone loves a restaurant that’s fresh and unique, and they write “this place is gnarly!”
In the 1980s, this word could be taken to mean both “excellent” and “disgusting.” Certainly, you don’t want a restaurant to be classified as the latter, and the NLP must be trained to recognize the difference in context.
Emotional AI Use Cases
As mentioned earlier, emotional AI’s impact is being felt in detect fraudulent activity and the growing need for improved security in various industries.
Let’s briefly look at a few key areas.
Approximately 35.8 million American adults admit to lying to their car insurers. That amounts to around 14 percent of such claims. In the UK, the Association of British Insurers states fraud costs over £1 billion each year.
Insurance companies can leverage speech recognition in emotional AI to detect whether a customer is telling the truth when submitting a claim.
Workplace and Road Safety
Emotional AI prevents workplace accidents by detecting signs of fatigue among employees and alert them to potential dangers.
For example, it monitors the faces of employees in a warehouse setting and, upon detecting signs of fatigue, suggests the worker takes a break before causing an accident.
The same applies to autonomous vehicles, wherein camera sensors monitor the driver’s blink rate, head position, and head angle for signs of drowsiness.
If the emotional AI detects signs of lethargy, it can then alert the driver by playing loud music or changing the temperature in the car.
Mental health conditions like depression and post-traumatic stress disorder can be spotted by analyzing speech patterns and facial expressions.
One study shows how machine learning algorithms using language analysis were 100% accurate at identifying teens who were likely to develop psychosis.
These tools already exist, and they’re incredibly powerful.
Appropriate and well-timed action is crucial in many areas of mental health. AI tools provide invaluable support to human providers and patients between appointments.
Retail and Customer Service
Retailers use facial recognition emotion AI technology in stores to capture demographic information and visitors’ mood and reactions.
If you pick up pair of shoes and smile, for example, they’ll note the positive reaction. If this happens again and again, they’ll know they have a solid product on their hands.
On the flip side, algorithms catch angry customers calling customer service lines from the beginning and direct them to trained agents. They also monitor the conversation in real-time and adjust the script accordingly.
This is just scratching the surface, really. There’s plenty of benefits to emotional AI, and no doubt still many questions about bias, privacy, and the overall efficacy of reading emotions.
Annotation and labeling for both video, image, speech, and text is therefore huge.
When your goal is to further improve the accuracy of AI’s emotional recognition – whether seen, spoken, or typed – and subsequent response, you need the help of real-life human transcribers.
They record what people say and how they express themselves, when and how they say it, and by whom.
When clients come to us for customized data collection and transcription, they’re trying to solve for the edge cases where automated speech recognition still struggles.
We therefore initiate natural data collection and err on the side of human transcription to ensure accuracy and inclusivity, and to handle complex environments and use cases.
We Can Get Your Emotional AI to Market
There are many variables to consider when it comes to optimizing your requirements for cost and delivery speed.
You need a provider that’s adaptable, flexible, and looking out for your best interests.
If they’re not deep diving into your end use case and offering a variety of solutions, they’re likely not the best fit.
At Summa Linguae, our data solutions experts work with you to understand exactly what level of transcription you need.
And if your requirements aren’t yet fully defined, we can help you choose the right solution.
Contact us now to get help with your emotional AI solution.
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