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

Researchers have developed a method to produce gradient boundaries in forced audio alignment by calculating confidence intervals from neural network ensembles, offering more accurate segment transitions and uncertainty indicators. This technique enhances model reliability and aids in identifying segments requiring review, with improved performance observed on the Buckeye and TIMIT corpora compared to single-model approaches.

Read the full article at arXiv cs.CL (NLP)

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