Audio Sentiment Neural Network with Semantic Smoothing
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Solution Overview
Problem
Existing systems poorly perform in determining nuanced emotional states from speech due to semantic overlap, intra-rater variability, and inter-rater differences, leading to inconsistent sentiment analysis.
Innovation Solution
A system that processes audio data to determine sentiment using a neural network trained with smoothed weights derived from semantic descriptor similarity data, accounting for rater variability and overlap, to provide accurate emotional state assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis systems are used to determine emotional states from speech, then the analysis process is simple, but the accuracy is poor due to semantic overlap and rater variability
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing multiple rater annotations before final sentiment determination. Multiple raters annotate the same audio samples to establish a ground truth sentiment label, which is then used to train the machine learning model. This preliminary annotation phase ensures high accuracy by accounting for semantic overlap and rater variability before the actual sentiment analysis occurs.
Solution Approach 2:
The patent introduces an intermediary mechanism - a machine learning model trained on multi-rater annotation data - that mediates between the complex variations in human speech expressions and the sentiment analysis task. This intermediary model learns to handle semantic overlap and rater variability by being trained on diverse annotations, thereby improving accuracy without requiring complex real-time processing during actual sentiment determination.
2Reliability
If multiple raters are used to annotate sentiment data, then the training data quality improves, but the time and resources required increase
Solution Approach 1:
The system applies partial action by using a sufficient number of raters to achieve reliable ground truth without requiring exhaustive annotation from all possible raters. The patent determines that a specific number of raters (e.g., 5-10 raters per audio sample) provides diminishing returns beyond a certain point, thus optimizing the balance between annotation effort and data quality by using just enough raters to establish reliable sentiment labels.
3Measurement precision
If semantic descriptor similarity data is incorporated into the model, then the sentiment determination accuracy improves, but the computational complexity increases
Solution Approach 1:
The system performs preliminary computation by pre-calculating semantic descriptor similarity data during the training phase rather than during real-time sentiment analysis. The machine learning model is trained on audio features along with pre-computed semantic similarity information, allowing the model to learn from these relationships without requiring complex real-time calculations during actual sentiment determination, thus reducing operational computational burden.
Data Source
AI summary
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.