Abstract
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.
Highlights
Audiologists and speech pathologists use speech perception tests to analyze speech comprehension in people who are learning to hear with hearing aids and cochlear implants
This section describes: (i) the design of the program; (ii) how stimulus-response pairs of words or sentences are formatted as two sequences of phonemes; (iii) how two sequences are aligned; (iv) how the F1-score is used to compute the accuracy of the stimulus phonemes; (v) how relative information transfer is used to assess the accuracy based on phonological features; (vi) how the preceding two metrics can be visualized for a set of stimulusresponse pairs; (vii) the different datasets used for testing; and (viii) program validation
Of the 361 stimulus-response pairs used for Figures 6–9, there were just two instances of double alignments
Summary
Audiologists and speech pathologists use speech perception tests to analyze speech comprehension in people who are learning to hear with hearing aids and cochlear implants. Phonemes are differentiated by how they are produced in the vocal tract, i.e. phonological features [1]. These features are place, manner, voicing and associated subtypes, and for vowels, these are height, place and associated subtypes. Speech tests are based on lists of words or sentences and are presented in a sound booth in the clinic, sometimes with noise, e.g. PBK-50 [2], AB [3], NU-6 [4], BKB [5], CUNY [6], HINT [7] and AzBio [8]. The challenge is to present information about phonemic comprehension in a manner that can be understood in real time
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
