The disclosure describes an audio-based emotion recognition system that is able to classify emotions in real-time. The emotion recognition system, according to some embodiments, adjusts the behavior of intelligent systems, such as a virtual coach, depending on the user's emotion, thereby providing an improved user experience. Embodiments of the emotion recognition system and method use short utterances as real-time speech from the user and use prosodic and phonetic features, such as fundamental frequency, amplitude, and Mel-Frequency Cepstral Coefficients, as the main set of features by which the human speech is characterized. In addition, certain embodiments of the present invention use One-Against-All or Two-Stage classification systems to determine different emotions. A minimum-error feature removal mechanism is further provided in alternate embodiments to reduce bandwidth and increase accuracy of the emotion recognition system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of adjusting an intelligent system based on the emotion of a user, comprising: obtaining audio data based on speech from a user of the intelligent system; extracting a plurality of features from the audio data; classifying the audio data based on one or more of the plurality of features, wherein an emotion associated with the speech is assigned to the audio data; and modifying instructions generated by the intelligent system based on the emotion. 2. The method of claim 1, wherein extracting a plurality of features comprises: reading the audio data; calculating a set of Mel-frequency Cepstral coefficients from the audio data; determining a set of FO values from the audio data; and calculating a mean, standard deviation, maximum, and minimum from the set of FO values. 3. The method of claim 2, further comprising: removing portions of the audio data corresponding to silences in the speech; and resampling the audio data. 4. The method of claim 1, wherein the emotion is selected from the group consisting of happiness, neutrality, anger, fear, sadness, and disgust. 5. The method of claim 1, wherein classifying the audio data comprises: classifying the audio data into a first class or a second class in a first stage classification, wherein the first class comprises positive emotions, wherein the second class comprises negative emotions; assigning the audio data to one of two second stage classifiers based on the first stage classification; and classifying the audio data in a second stage classification. 6. The method of claim 1, further comprising: training a classifier to classify the audio data. 7. The method of claim 6, wherein training the classifier comprises: selecting a support vector machine kernel to generate a classification model; discriminating the plurality of features; performing a cross-validation of the discriminated features to generate a confusion matrix associated with the model; selecting sigma and complexity values; preparing training and testing indices and labels; applying the support vector machine kernel to the training data; testing and training the model; updating the confusion matrix for the model; calculating the accuracy of the confusion matrix; and saving the model based on the discriminated features and the updated confusion matrix. 8. The method of claim 7, wherein discriminating the plurality of features comprises: ordering the plurality of features based on an ability of each feature to discriminate the audio data into one of a plurality of emotions; and removing a lowest ranked feature. 9. An intelligent system for generating prompts based on the emotions of a user, the intelligent system comprising: an audio capture device for generating audio data; a processor; and a set of executable instructions stored on memory, the instructions comprising: a feature extraction module, and a classification module; wherein the processor executes the instructions to: extract a plurality of features from the audio data; classify the audio data with an emotion using at least a portion of the plurality of features. 10. The intelligent system of claim 9, further comprising: an image capture device for generating video data; a second set of executable instructions comprising a motion evaluator; wherein the processor executes the second set of instructions to: identify a motion performed by the user as correct or incorrect. 11. The intelligent system of claim 10, further comprising: a user interface, wherein the user interface displays instructions to the user, wherein the instructions are based on the identification of the motion and the emotion classification.