AI Article Synopsis
- The study introduces a new method, using naive discriminative learning (NDL), to measure pronunciation distances, which can be important for various areas of linguistics.
- NDL offers a more advanced and flexible approach compared to the traditional Levenshtein algorithm, as it is informed by cognitive theories and can produce asymmetrical distances.
- Validation efforts showed that NDL distances correlated well with native speakers' perceptions of accented speech, achieving correlation rates between 0.7 and 0.8, and can also integrate additional acoustic features beyond just sound segments.
Article Abstract
In this study we develop pronunciation distances based on naive discriminative learning (NDL). Measures of pronunciation distance are used in several subfields of linguistics, including psycholinguistics, dialectology and typology. In contrast to the commonly used Levenshtein algorithm, NDL is grounded in cognitive theory of competitive reinforcement learning and is able to generate asymmetrical pronunciation distances. In a first study, we validated the NDL-based pronunciation distances by comparing them to a large set of native-likeness ratings given by native American English speakers when presented with accented English speech. In a second study, the NDL-based pronunciation distances were validated on the basis of perceptual dialect distances of Norwegian speakers. Results indicated that the NDL-based pronunciation distances matched perceptual distances reasonably well with correlations ranging between 0.7 and 0.8. While the correlations were comparable to those obtained using the Levenshtein distance, the NDL-based approach is more flexible as it is also able to incorporate acoustic information other than sound segments.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886970 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075734 | PLOS |
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