They offer an API sandbox, along with free monthly usage for live testing. Their demo video outlines some creative use cases in virtual reality scenarios. The software can be tested using the Intraface iOS app.
The Findface software utilizes the NtechLab face recognition algorithm to recognize 7 basic emotions as well as 50 complex attributes. There are many sentiment analysis APIs out there that provide categorization or entity extraction, but the APIs listed below specifically respond with an emotional summary given a body of plain text. Powered by the supercomputer IBM Watson, The Tone Analyzer detects emotional tones, social propensities, and writing styles from any length of plain text.
Input your own selection on the demo to see tone percentile, word count, and a JSON response. Backed by decades of language-psychology research, the Receptiviti Natural Language Personality Analytics API uses a process of target words and emotive categories to derive emotion and personality from texts.
You can enter a URL to receive a grade of positive, mixed, or negative overall sentiment. Synesketch is basically the iTunes artwork player for the written word. A few third-party apps have already been constructed with this open source software to recognize and visualize emotion from Tweets, speech, poetry, and more.
The tool takes a body of text and analyzes for emotional breadth, intensity, and comparison with other texts. Looks to be a cool service for automating in-house research to optimize smart content publishing. The Repustate Sentiment Analysis process is based in linguistic theory, and reviews cues from lemmatization, polarity, negations, part of speech, and more to reach an informed sentiment from a text document.
Lastly, humans also interact with machines via speech. There are plenty of speech recognition APIs on the market, whose results could be processed by other sentiment analysis APIs listed above.
Perhaps this is why an easy-to-consume web API that instantly recognizes emotion from recorded voice is rare. Use cases for this tech could be:. Given a database of speech recordings, the Vokaturi software will compute percent likelihoods for 5 emotive states: neutrality, happiness, sadness, anger, and fear. They provide code samples for working in C and Python. Machine emotional intelligence is still evolving, but the future could soon see targeted ads that respond to not only our demographic age, gender, likes, etc.
For point of sale advertising, this information could be leveraged to nudge sales when people are most emotionally vulnerable, getting into some murky ethical territory. There are of course data privacy legalities any API provider or consumer should be aware of before implementation. We are only on the tip of the iceberg when it comes to machine human interaction, but cognitive computing technologies like these are exciting steps toward creating true machine emotional intelligence.
Respond below or add to this Product Hunt list. Bill Doerrfeld is a tech journalist and API thought leader. He oversees the content direction and publishing schedule for the NordicAPIs blog. Advanced emotion AI solutions like those provided by Affectiva or Kairos can measure the following emotion metrics: joy, sadness, anger, contempt, disgust, fear, and surprise. Additional software features may include facial identification and verification, age and gender detection, ethnicity and multi-face detection, and much more.
Recognizing emotion from speech has become the next stage of natural language processing , adding new value to the human-computer interaction. Voice emotion recognition software enables to process audio files containing human voice and analyzes not what is said, but how it is said, by extracting the paralinguistic features and observing changes in tone, loudness, tempo, voice qualities to interpret these as human emotions, distinguish gender, age, etc.
Voice analysis and emotion detection are already used by major brands in many industries, including market research, call centers, social robotics, healthcare, and many more. Voice recognition software works similarly to facial emotion recognition. Nemesysco developed a technology named Layered Voice Analysis LVA to detect stress and deception in the speech by leveraging above uncontrollable biomarkers to trace the genuine emotion of the speaker regardless of their language and tone of voice.
Insights this technology can provide are invaluable for customer experience management, forensic science, security and fraud protection in banking and insurance, and many other industries.
Logically, emotionally intelligent machines will need to capture all verbal and nonverbal cues to estimate the emotional state of a person precisely, using face or voice, or both.
The majority of the emotion AI developers are unanimous in declaring that the main goal of multimodal emotion recognition is to make the human-machine communication more natural. However, there is a lot of controversy around this topic. Do we really want our emotions to be machine-readable? Today we shall focus on some positive examples of the emotional AI application. Emotional support. Nurse bots can remind older patients to take their medication and 'talk' with them every day to monitor their overall wellbeing.
Mental health treatment. Emotion AI-powered chatbots can imitate a therapist or a counselor and help automate talk therapy and accessibility. There are also mood tracking apps like Woebot that help people manage mental health through short daily chat conversations, mood tracking, games, curated videos, etc. AI as medical assistants. Emotion AI can assist doctors with diagnosis and intervention and provide better care. Entirely virtual digital humans are not designed to simply answer questions like Siri or Alexa, but are supposed to look and act like humans, show emotions, have their unique personalities, learn and have real conversations.
Understanding consumer emotional responses to brand content is crucial for reaching the marketing goals. Advertising research. Emotion is the core of effective advertising: the shift from negative to positive emotions can ultimately increase sales.
From the graph See Fig. We also observe that disgust and sadness are closer to neutral with regards to energy although exceptions do exist. As mentioned before, anger and fear occupy the high power space and sadness and neutral occupy the low power space while being scattered pace-wise.
The solution pipeline for this study is depicted in the schematic shown in Fig. The raw signal is the input which is processed as shown.
At first the 2D features were extracted from the datasets and converted into 1-D form by taking the row means. A measure of noise was added to the raw audio for 4 of our datasets except CREMA-D as the others were studio recording and thus cleaner.
The features were then extracted from those noisy files and our dataset was augmented with them. As some of the models were overfitting the data, and taking into consideration a large number of features in 1D we tried dimensionality reduction to check overfitting and trained the models again.
For the SVM and XGB models the model was simply split into train-test data in the ratio of and validated using cross-validation with 5-folds.
This model consisted of 1 Convolution layer of 64 channels and same padding followed by a dense layer and the output layer. This model was constructed in a similar format as VGG, but the last 2 blocks of 3 convolution layers were removed to reduce complexity.
The result is based on the accuracy metrics in which there is a comparison between predicted values and the actual values. From confusion metrics, we have calculated accuracy as follows:. The model was trained on training data and tested on test data with different numbers of epochs starting from 50 to , and The accuracies were compared among all models viz. We find from Fig. On the other hand, CNN-1D Shallow gave much better results on account of it being more stable with its train, validation and test accuracies being closer to each other, though its testing accuracy was a little lower than the CNN-1D.
In order to rectify the overfitting of the models we used a dimensionality reduction approach. PCA technique was employed for dimensionality reduction in 1D features and dimensions were reduced from to with an explained variance of From this we deduced that our dataset is simply not big enough for a complex model to perform well and realised the solution was limited by lack of a larger data volume.
We tested the developed models on user recordings, from the test results we have the following observations. The accuracies certainly improved on reducing the number of classes, but this introduced another problem with regards to class imbalance. After, combining anger-disgust and sad-boredom, the model developed a high bias towards the anger-disgust. This may have happened because the number of instances of anger-disgust became disproportionately more than the other labels. So, it was decided to stick with the older model.
Through this project, we showed how we can leverage Machine learning to obtain the underlying emotion from speech audio data and some insights on the human expression of emotion through voice.
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