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

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: Not quantified in abstract; described as 'slight overall improvement'

• Primary Outcome: Gradient phonetic segment boundaries with associated confidence intervals produced via 10-model neural network ensemble; slight overall improvement in boundary placement accuracy over single-model forced alignment on Buckeye and TIMIT corpora

• Nnt Or Sensitivity: Not applicable — methodological/computational study; no NNT, sensitivity, or specificity reported

• Confidence Interval: 97.85% confidence interval around median boundary placement used as the gradient boundary range (methodological parameter, not a performance CI)

Clinical Application

Feasibility for research use is reasonable given practical output formats (JSON, Praat TextGrids). Clinical deployment would require validation on disordered speech, non-American English accents, and assessment of computational requirements in clinical workflow contexts. The 10-fold increase in model runs may present barriers in resource-limited settings. This study has no direct PBS, TGA, or RACGP relevance. In the Australian context, potential indirect relevance exists for: (1) speech-language pathology research using acoustic analysis tools (e.g., in university clinics, NHMRC-funded speech science research); (2) Australian English phonetics research, where the American English corpus validation may limit direct applicability without re-validation on Australian English speech data; (3) clinical research in motor speech disorders or voice pathology where precise acoustic boundary detection is methodologically important. No regulatory or reimbursement implications apply. Not directly applicable to clinical patient populations. Potentially relevant to speech-language pathologists and clinical researchers using forced alignment tools for acoustic analysis of speech in research settings (e.g., motor speech disorders, voice analysis, language acquisition research).

Abstract

Forced alignment is a common tool to align audio with orthographic and phonetic transcriptions. Most forced alignment tools provide only point-estimates of boundaries. The present project introduces a method of producing gradient boundaries by deriving confidence intervals using neural network ensembles. Ten different segment classifier neural networks were previously trained, and the alignment process is repeated with each classifier. The ensemble is then used to place the point-estimate of a boundary at the median of the boundaries in the ensemble, and the gradient range is placed using a 97.85 % confidence interval around the median constructed using order statistics. Gradient boundaries are taken here as a more realistic representation of how segments transition into each other. Moreover, the range indicates the model uncertainty in the boundary placement, facilitating tasks like finding boundaries that should be reviewed. As a bonus, on the Buckeye and TIMIT corpora, the ensemble boundaries show a slight overall improvement over using just a single model. The gradient boundaries can be emitted during alignment as JSON files and a main table for programmatic and statistical analysis. For familiarity, they are also output as Praat TextGrids using a point tier to represent the edges of the boundary regions.

References

1. 1.Kelley, M. C. (2026). Gradient boundaries through confidence intervals for forced alignment estimates using model ensembles. Phonetica. https://doi.org/10.1121/10.0039842