Gradient boundaries through confidence intervals for forced alignment estimates using model ensembles.

Area of Science:

• Speech processing
• Computational linguistics
• Machine learning

Background:

• Forced alignment tools typically provide single point-estimates for audio segment boundaries.
• Accurate boundary detection is crucial for various speech analysis tasks.

Purpose of the Study:

• To develop a novel method for producing gradient boundaries in forced alignment using neural network ensembles.
• To represent segment transitions more realistically and quantify boundary placement uncertainty.

Main Methods:

• Utilized an ensemble of ten previously trained segment classifier neural networks.
• Repeated the alignment process with each network and aggregated results using order statistics.
• Derived confidence intervals to establish gradient boundary ranges around a median estimate.

Main Results:

• The ensemble method produced gradient boundaries, offering a more realistic representation of segment transitions.
• A 97.85% confidence interval was used to define the gradient range, indicating model uncertainty.
• Ensemble boundaries showed slight overall improvement in accuracy on the Buckeye and TIMIT corpora compared to single models.

Conclusions:

• Gradient boundaries provide a more nuanced and realistic representation of speech segment transitions.
• The derived confidence intervals quantify model uncertainty, aiding in the identification of boundaries requiring manual review.
• This approach offers potential improvements in forced alignment accuracy and facilitates further statistical analysis.