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

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.

Preliminary Evaluation of Automated Speech Recognition Apps for the Hearing Impaired and Deaf

“…In addition, the use of ASR could open venues to improved (automated) scoring methods in audiology tests. Ratnanather et al ( 51 ) demonstrated how one can automate the alignment of phonemes based on the minimum edit distance between the source speech and the utterances of the subject in real time. Visualizing this alignment may provide insights to clinicians about what phonological errors are made.…”

Consonant Perception in Connected Syllables Spoken at a Conversational Syllabic Rate

“…Responses to open-set sentences, especially when presented in background noise, include insertions (i.e., reporting words or phonemes that were not presented, or false starts such as "um") and deletions (i.e., not reporting words or phonemes that were presented), which makes it extremely difficult to create a one-to-one mapping of response phonemes to stimulus phonemes that is necessary for analyzing consonant feature errors. Automatic phoneme alignment algorithms have been developed for open-set responses to sentence-length stimuli (Bernstein et al, 1994(Bernstein et al, , 2021Ratnanather et al, 2022) in order to generate consonant confusion matrices (CMs) for sentence stimuli. However, consonant feature analysis based on such CMs is confounded by the context information in meaningful words and sentences.…”