Abstract: A method for adaptive voice interaction includes monitoring voice communications between a service recipient and a service representative, measuring a set of voice communication features based upon the voice communications between the service recipient and the service representative, analyzing the set of voice communication features to generate emotion metric values, and generating a response based on the analysis of the set of voice communication features.

Abstract: The invention described here uses a Mouth Phoneme Model that relates phonemes and visemes using audio and visual information. This method allows for the direct conversion between lip movements and phonemes, and furthermore, the lip reading of any word in the English language. Speech API was used to extract phonemes from audio data obtained from a database which consists of video and audio information of humans speaking a word in different accents. A machine learning algorithm similar to WEKA (Waikato Environment for Knowledge Analysis) was used to train the lip reading system.

Abstract: A method for adaptive voice interaction includes monitoring voice communications between a service recipient and a service representative, measuring a set of features based upon the voice communications, and analyzing the set of features to generate emotion metric values.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system and the probable emotional state is communicated to a user.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system and the probable emotional state is communicated to a user.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system, and the probable emotional state is communicated to a user.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system, and the probable emotional state is communicated to a user.