Voice Signal Clustering for Speaker Attribute Estimation
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Solution Overview
Problem
Existing speaker attribute estimation models require a large amount of training data with correct speaker attributes, making it costly and leading to decreased estimation accuracy due to overfitting when sufficient data is not available.
Innovation Solution
An estimation apparatus that clusters voice signals and uses a speaker attribute estimation model to identify clusters and estimate attributes based on feature similarities, allowing for accurate estimation even with insufficient training data by leveraging the most common attributes within clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large amount of training data with correct speaker attributes is used to construct a robust estimation model, then the reliability of speaker attribute estimation is improved, but the cost of data preparation increases and the device complexity increases
Solution Approach 1:
The patent segments the training data into two distinct parts: voice data with speaker attributes (first voice data) and voice data without speaker attributes (second voice data). This segmentation allows the model to learn different aspects of speaker characteristics from each data type, achieving robust estimation without requiring all training data to be manually annotated with speaker attributes, thereby reducing data preparation complexity while maintaining reliability.
2Ease of manufacture
If a sufficient amount of training data with speaker attributes is not available, then the cost of data preparation is reduced, but the measurement precision of speaker attribute estimation decreases due to overfitting
Solution Approach 1:
The patent introduces an intermediary component - the speaker verification model trained on second voice data without speaker attributes. This intermediary model learns general speaker characteristics and voice features that serve as a foundation for the speaker attribute estimation model. By using this intermediary training step, the system achieves better estimation accuracy without requiring sufficient manually annotated training data, thus avoiding overfitting while maintaining ease of data preparation.
3Device complexity
If only a speaker attribute estimation model trained on limited annotated data is used, then the device complexity is reduced, but the reliability of estimation for unknown speakers decreases
Solution Approach 1:
The patent applies preliminary action by training a speaker verification model on second voice data without speaker attributes before training the speaker attribute estimation model. This preliminary training step pre-processes the model to learn general speaker verification capabilities and voice features, which then serve as a strong foundation for the subsequent attribute estimation task. This two-stage preliminary preparation enables the model to generalize better to unknown speakers while keeping the overall device complexity manageable.
Data Source
AI summary
An estimation apparatus clusters a group of voice signals including a voice signal having a speaker attribute to be estimated into a plurality of clusters. Subsequently, the estimation apparatus identifies, from the plurality of clusters, a cluster to which the voice signal to be estimated belongs. Next, the estimation apparatus uses a speaker attribute estimation model to estimate speaker attributes of respective voice signals in the identified cluster. After that, the estimation apparatus estimates an attribute of the entire cluster, by using an estimation result of the speaker attributes of the voice signals in the identified cluster, and outputs an estimation result of the speaker attribute of the entire cluster, as an estimation result of the speaker attribute of the voice signal to be estimated.