A system including sensors configured to provide physiological markers of a developer and a controller configured provide information indicative of a user experience to the developer while receive signals from the sensors. The controller is configured to utilize cognitive analysis determine developer emotion responses as the developer receives the user experience. The controller compares a developer emotion classification with a user emotion classification of a user as the user generated the user experience. The system generates a prioritized backlog to identify points where emotion responses between user and developer are in common, or where emotion responses between user and developer differ.

A system including sensors configured to provide physiological markers of a developer and a controller configured provide information indicative of a user experience to the developer while receive signals from the sensors. The controller is configured to utilize cognitive analysis determine developer emotion responses as the developer receives the user experience. The controller compares a developer emotion classification with a user emotion classification of a user as the user generated the user experience. The system generates a prioritized backlog to identify points where emotion responses between user and developer are in common, or where emotion responses between user and developer differ.

A method of suppressing wind noise of a microphone and/or an electronic device are disclosed. The method of suppressing wind noise of a microphone includes receiving an audio signal, obtaining a frequency spectrum of the audio signal and a power spectrum of the audio signal, determining a wind noise power spectrum of the audio signal based on the power spectrum, determining a wind noise suppression gain based on the wind noise power spectrum and the power spectrum, correcting the frequency spectrum according to the determined wind noise suppression gain, and converting the corrected frequency spectrum into a time domain to obtain a corrected audio signal.

A method of suppressing wind noise of a microphone and/or an electronic device are disclosed. The method of suppressing wind noise of a microphone includes receiving an audio signal, obtaining a frequency spectrum of the audio signal and a power spectrum of the audio signal, determining a wind noise power spectrum of the audio signal based on the power spectrum, determining a wind noise suppression gain based on the wind noise power spectrum and the power spectrum, correcting the frequency spectrum according to the determined wind noise suppression gain, and converting the corrected frequency spectrum into a time domain to obtain a corrected audio signal.

According to some aspects, disclosed methods and systems may include having a user input one or more speech commands into an input device of a user device. The user device may communicate with one or more components or devices at a local office or headend. The local office or the user device may transcribe the speech commands into language transcriptions. The local office or the user device may determine a mood for the user based on whether any of the speech commands may have been repeated. The local office or the user device may determine, based on the mood of the user, which content asset or content service to make available to the user device.

A method and system for detecting one or more speech features in speech audio data includes receiving speech audio data, performing preprocessing on the speech audio data to prepare the speech audio data for use as an input into one or more models that detect one or more speech features, providing the preprocessed speech audio data to a stacked machine learning model, and analyzing the preprocessed speech audio data via the stacked ML model to detect the one or more speech features. The stacked ML model includes a feature aggregation model, a sequence to sequence model, and a decision-making model.

A method and system for detecting one or more speech features in speech audio data includes receiving speech audio data, performing preprocessing on the speech audio data to prepare the speech audio data for use as an input into one or more models that detect one or more speech features, providing the preprocessed speech audio data to a stacked machine learning model, and analyzing the preprocessed speech audio data via the stacked ML model to detect the one or more speech features. The stacked ML model includes a feature aggregation model, a sequence to sequence model, and a decision-making model.

Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.

Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.

A method of processing audio data, an electronic device, and a storage medium, which relates to a field of artificial intelligence, in particular to a field of speech processing technology. The method includes: processing spectral data of the audio data to obtain a first feature information; obtaining a fundamental frequency indication information according to the first feature information, wherein the fundamental frequency indication information indicates valid audio data of the first feature information and invalid audio data of the first feature information; obtaining a fundamental frequency information and a spectral energy information according to the first feature information and the fundamental frequency indication information; and obtaining a harmonic structure information of the audio data according to the fundamental frequency information and the spectral energy information.