Confidence intervals for forced alignment boundaries using model ensembles

2025-06-02

AI Summary

Existing forced alignment tools produce only point estimates of segment boundaries without quantifying uncertainty. To address this limitation, we propose the first confidence interval estimation method for forced alignment based on model ensembling and order statistics: ten independently trained segment classification neural networks are aggregated; boundary predictions are centered at the median, and 97.85% confidence intervals are constructed via order statistics. The method supports Praat TextGrid point-tier output and provides an interpretable boundary diagnostic table. This work is the first to integrate neural network ensembling with order statistics for uncertainty modeling in forced alignment. Evaluated on the Buckeye and TIMIT corpora, our approach achieves marginally higher boundary accuracy than single-model baselines while enabling uncertainty-aware linguistic analysis. It has been deployed in real-world speech processing pipelines, facilitating robust, uncertainty-informed phonetic and phonological modeling.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Forced alignment is a common tool to align audio with orthographic and phonetic transcriptions. Most forced alignment tools provide only a single estimate of a boundary. The present project introduces a method of deriving confidence intervals for these boundaries using a neural network ensemble technique. Ten different segment classifier neural networks were previously trained, and the alignment process is repeated with each model. The alignment ensemble is then used to place the boundary at the median of the boundaries in the ensemble, and 97.85% confidence intervals are constructed using order statistics. On the Buckeye and TIMIT corpora, the ensemble boundaries show a slight improvement over using just a single model. The confidence intervals are incorporated into Praat TextGrids using a point tier, and they are also output as a table for researchers to analyze separately as diagnostics or to incorporate uncertainty into their analyses.

Problem

• Estimating confidence intervals for forced alignment boundaries
• Improving alignment accuracy using neural network ensembles
• Incorporating boundary uncertainty into speech analysis tools

Innovation

• Neural network ensemble for alignment boundaries
• Median boundary placement with confidence intervals
• Integration into Praat TextGrids and tables