Amazon Technologies, Inc.

Daniel Kenneth Bone

Chao Wang

Viktor Rozgic

Bilyana Slavova

Narendra Gyanchandani

Hanhan Wang

Njenga Kariuki

Described herein is a system for emotion detection in audio data using a speaker's baseline. The baseline may represent a user's speaking style in a neutral emotional state. The system is configured to compare the user's baseline with input audio representing speech from the user to determine a emotion of the user. The system may store multiple baselines for the user, each associated with a different context (e.g., environment, activity, etc.), and select one of the baselines to compare with the input audio based on the contextual situation.

Emotion detection using speaker baseline

A device with a microphone acquires audio data of a user's speech. A neural network accepts audio data as input and provides sentiment data as output. The neural network is trained using training data based on input from raters who provide votes as to which sentiment descriptors they think are associated with a sample of speech. A vote by a rater assessing the sample for a particular semantic descriptor is distributed to a plurality of semantically similar semantic descriptors. Semantic descriptor similarity data indicates relative similarity between possible semantic descriptors in the semantic space. The distributed partial votes may be aggregated to produce training data comprising samples of speech and weights of corresponding semantic descriptors. The training data is then used to train the neural network. For example, the neural network may be trained with the training data using per-instance cosine similarity loss or correlational loss.

System to determine sentiment from audio data

A device with a microphone acquires audio data of a user's speech. That speech comprises utterances, that together comprise a session. The audio data is processed to determine sentiment data indicative of perceived emotional content of the speech as conveyed by individual utterances of the user. That information is then used to determine the emotional content of the session. For example, the information may include several words describing the overall and outlying emotions of the session. Numeric metrics may also be determined, such as activation and valence. A user interface may present the words and metrics to the user. The user may use this information to assess their state of mind, facilitate interactions with others, and so forth.

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Somerville, MA, United States of America

Daniel Kenneth Bone