Speech Valence Detection via Emotion Word Segmentation
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Solution Overview
Problem
Current methods for detecting emotional valence from speech signals face limitations in classification performance, as they do not effectively differentiate between speech segments with and without emotion words, leading to inaccurate valence detection in real-world scenarios where emotions are interspersed with neutral segments.
Innovation Solution
A processor-implemented method and system that split input speech signals into segments based on the presence of emotion words, using a language-specific emotion words dictionary to select appropriate segments for training classifiers, thereby isolating emotion words-based and non-emotion words-based segments for training, which are then used to detect emotional valence in input speech signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If speech signals are split into many shorter segments for training, then the quantity of training data increases, but the measurement precision of emotional valence detection deteriorates because segments may not carry the same emotion as the overall story
Solution Approach 1:
The patent applies segmentation by dividing speech signals into segments based on the presence of emotion words. Instead of uniform splitting, segments are created dynamically around emotion words using a window approach (e.g., ±1 second), ensuring each segment captures complete emotional expressions while maintaining appropriate quantity for training.
Solution Approach 2:
The patent implements local quality by treating segments containing emotion words differently from other segments. Emotion word-based segments are extracted and handled separately with different training strategies, allowing the system to focus computational resources on segments with higher emotional information density while maintaining overall detection accuracy.
2Productivity
If all speech segments are used for training classifiers, then the productivity of training data utilization increases, but the reliability of emotional valence detection deteriorates due to inclusion of neutral segments that do not carry emotional information
Solution Approach 1:
The patent extracts segments containing emotion words from the overall speech signal using a language-specific emotion words dictionary. This extraction process isolates emotionally informative segments for targeted training, removing neutral segments that would otherwise dilute the training quality while maintaining high productivity through efficient segment selection.
Solution Approach 2:
The patent changes the parameter of segment selection from uniform random sampling to emotion-word-based selective sampling. By using emotion words as selection criteria and adjusting segment window parameters (e.g., ±1 second around emotion words), the system optimizes both training productivity and detection reliability.
3Device complexity
If emotion words are not differentiated from other words, then the device complexity is reduced, but the measurement precision of emotional valence detection deteriorates
Solution Approach 1:
The patent introduces an intermediary component: a language-specific emotion words dictionary. This dictionary acts as a mediator between the raw speech signal and the classification process, enabling precise identification of emotionally informative segments without requiring complex deep learning architectures, thus maintaining relatively simple device complexity while improving measurement precision.
Data Source
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AI summary
An important task in several wellness applications is detection of emotional valence from speech. Two types of features of speech signals are used to detect valence: acoustic features and text features. Acoustic features are derived from short frames of speech, while text features are derived from the text transcription. Present disclosure provides systems and methods that determine the effect of text on acoustic features. Acoustic features of speech segments carrying emotion words are to be treated differently from other segments that do not carry such words. Only specific speech segments of the input speech signal are considered based on a dictionary specific to a language to assess emotional valence. A model trained (or trained classifier) for specific language either by including the acoustic features of the emotion related words or by omitting it is used by the system for determining emotional valence in an input speech signal.