SYSTEMS AND METHODS FOR RECOMMENDING RESPONSES

摘要

Various of the disclosed embodiments concern systems and methods for identifying and recommending interesting user responses that are obtained by an interactive device (e.g., audio responses to a virtual character as part of a virtual interaction). In some embodiments, a user may interact with one or more virtual characters via a mobile device, tablet, desktop computer, or the like. During the interaction, the user may respond to one or more questions posed by the virtual characters or to contexts presented by the interactive device. The system may record these user responses, analyze the audio data to extract one or more features, and prepare a ranking of the user responses. The extracted features can be augmented with human-generated metadata or ground truth values. A reviewer can review, share, etc., the user response.

主权项

1. A computer-implemented method for recommending interesting user responses produced by a user and obtained by an interactive device comprising:
receiving, from the interactive device, a user response including an audio waveform; computing a textual hypothesis of the audio waveform, the textual hypothesis including a transcription of words identified in the audio waveform; extracting a feature from the audio waveform, the textual hypothesis, or both; generating a metric value for the feature, the metric value representing interest level of the feature; weighting the metric value based on:
a general language model that includes a generic corpus of ground truth feature values that indicate how user responses should be analyzed;a public language model that includes a public corpus of ground truth feature values derived from user responses produced by other users;a personal language model that includes a personal corpus of ground truth feature values derived from user responses previously produced by the user; andcontextual factors that indicate whether the user response should be characterized as interesting; and summing the weighted metric value with all other weighted metric values associated with features extracted from the user response, thereby generating a cumulative metric value that represents interest level of the user response as a whole.