Text-Speech Alignment: A Robin Hood Approach for Endangered Languages

Forced alignment automatically aligns audio recordings of spoken language with transcripts at the level of individual sounds, greatly reducing the time required to prepare data for linguistic analysis. However, existing algorithms are mostly trained on a few well-documented languages. We test the performance of three algorithms against manually aligned data on data from a highly endangered language. At least some tasks, unsupervised alignment (either based on English or trained from a small corpus) is sufficiently reliable for it to be used on legacy data for low-resource languages. Descriptive phonetic work on vowel inventories and prosody can be accurately captured by automatic alignment with minimal training data. Underutilized legacy data exist for many endangered languages. This creates both a need and an opportunity to leverage new technology

A Robin Hood approach to forced alignment: English-trained algorithms and their use on Australian languages

Forced alignment automatically aligns audio recordings of spoken language with transcripts at the segment level, greatly reducing the time required to prepare data for phonetic analysis. However, existing algorithms are mostly trained on a few well-documented languages. We test the performance of three algorithms against manually aligned data. For at least some tasks, unsupervised alignment (either based on English or trained from a small corpus) is sufficiently reliable for it to be used on legacy data for low-resource languages. Descriptive phonetic work on vowel inventories and prosody can be accurately captured by automatic alignment with minimal training data. Consonants provided significantly more challenges for forced alignment

Gradient prosodic boundary strength in syntactic disambiguation

Unlike syntactic structure, prosodic structure is typically thought of as non-recursive, with categorical boundaries delineating constituents within a rudimentary hierarchy. However, several experimental studies have demonstrated that prosodic boundaries are not produced categorically, and listeners are sensitive to these gradient variations in production, therefore suggesting that prosodic structure is more complex than normally assumed. While intriguing, it remains unclear whether such variability in boundary production is linguistically meaningful. Here, we explicitly test whether variability within a single class of prosodic boundary can be used to disambiguate sentences containing coordinative structures having multiple possible meanings. Interestingly, a majority of participants reliably employed gradient differences in pause duration to differentiate these ambiguous stimuli, demonstrating conclusively that listeners are not only able to perceive non-categorical variation in prosodic boundary strength, but that this variation can be linguistically meaningful. These results therefore indicate that prosodic structure may be less rigid than previously thought