An automated tool for comparing phonetic transcriptions

Bailey, Dallin J.; Atkins, Marisha Speights; Mishra, Ishaan; Li, Sicheng; Luan, Yaoxuan; Seals, Cheryl

doi:10.1080/02699206.2021.1896783

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…These problems are likely not to occur with word lists or nearly complete sentences which may be more helpful in pinpointing areas of weaknesses for auditory training. Others have used MED for aligning phonetic transcriptions of words based on phonological features ( 59 ) and fuzzy string matching with a novel metric for sentences ( 60 ), both of which are available as open source. Future work should also explore using costs from confusion matrices from people with normal hearing listening to sentences as opposed to words.…”

Section: Discussionmentioning

confidence: 99%

Visualization of Speech Perception Analysis via Phoneme Alignment: A Pilot Study

et al. 2022

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Objective: Speech tests assess the ability of people with hearing loss to comprehend speech with a hearing aid or cochlear implant. The tests are usually at the word or sentence level. However, few tests analyze errors at the phoneme level. So, there is a need for an automated program to visualize in real time the accuracy of phonemes in these tests.Method: The program reads in stimulus-response pairs and obtains their phonemic representations from an open-source digital pronouncing dictionary. The stimulus phonemes are aligned with the response phonemes via a modification of the Levenshtein Minimum Edit Distance algorithm. Alignment is achieved via dynamic programming with modified costs based on phonological features for insertion, deletions and substitutions. The accuracy for each phoneme is based on the F1-score. Accuracy is visualized with respect to place and manner (consonants) or height (vowels). Confusion matrices for the phonemes are used in an information transfer analysis of ten phonological features. A histogram of the information transfer for the features over a frequency-like range is presented as a phonemegram.Results: The program was applied to two datasets. One consisted of test data at the sentence and word levels. Stimulus-response sentence pairs from six volunteers with different degrees of hearing loss and modes of amplification were analyzed. Four volunteers listened to sentences from a mobile auditory training app while two listened to sentences from a clinical speech test. Stimulus-response word pairs from three lists were also analyzed. The other dataset consisted of published stimulus-response pairs from experiments of 31 participants with cochlear implants listening to 400 Basic English Lexicon sentences via different talkers at four different SNR levels. In all cases, visualization was obtained in real time. Analysis of 12,400 actual and random pairs showed that the program was robust to the nature of the pairs.Conclusion: It is possible to automate the alignment of phonemes extracted from stimulus-response pairs from speech tests in real time. The alignment then makes it possible to visualize the accuracy of responses via phonological features in two ways. Such visualization of phoneme alignment and accuracy could aid clinicians and scientists.

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Section: Discussionmentioning

confidence: 99%