Training speech pathology students in the perceptual evaluation of speech in patients with cleft palate: Reliability results and the students' perspective

The Role of EGG in Identifying Prevocalic Glottal Stop

OBJECTIVE

The aim of the study is to investigate the use of incidences and characteristics of Prevocalic Electroglottographic Signal (PVES) derived from electroglottography (EGG) in characterizing glottal stops (GS) in cleft palate speech.

METHODS

Mandarin nonaspirated monosyllabic first-tone words were used for the speech sampling procedure. A total of 1680 utterances (from 83 patients with repaired cleft palates) were divided into three categories based on the results of auditory-perceptual evaluation of recorded speech sounds by three independent reviewers: [Category A (absence of GS agreed by all three reviewers) (n = 1192 tokens), Category B (two out of three reviewers agreed on the presence of a GS) (n = 181 tokens) and Category C (all three reviewers agreed on the presence of a GS) (n = 307 tokens)]. The EGG signals of the 1680 utterances were analyzed using a MATLAB program to automatically mark the instances of PVES (amplitude and time-interval) in the GS utterances.

RESULTS

The result showed that the incidence of EGG PVES presented good positive correlation with auditory-perceptual evaluation (r = 0.703, P＜0.000). Statistical analysis revealed a significant difference in mean PVES amplitude among different groups (P＜0.05). There was a significant distinction in the time interval between groups A and B, as well as in groups A and C (P＜0.05).

CONCLUSIONS

The study suggests PVES can be an objective means of identifying GS in cleft palate speech. It also indicates that proportion of amplitude and time interval of PVES tend to be positively correlate with subjective assessment.

Effect of perceptive-auditory training on the classification of speech hypernasality

PURPOSE

To analyze the effect of auditory-perceptual training by inexperienced speech-language pathologists in the classification of hypernasality in individuals with cleft lip and palate and compare their classification of hypernasality individually, with the gold standard evaluation, before and after this training.

METHODS

Three inexperienced speech-language pathologists used a four-point scale to assess 24 high-pressure speech samples from individuals with cleft lip and palate, before and after auditory-perceptual training. The speech samples corresponded to six samples of each degree of hypernasality. The speech-language pathologists received auditory-perceptual training during the assessments. They had access to anchor samples and immediate feedback of correct answers regarding the degree of hypernasality in training.

RESULTS

There was no significant difference in the overall percentage of correct answers when comparing before and after the auditory-perceptual training. There was a significant association and agreement of the three evaluators with a gold standard evaluation after training, with an increase in agreement for a single evaluator for absent and mild degrees of hypernasality. The dichotomous analysis of the data showed an increase in the Kappa Index of Agreement of this evaluator. Although there was an increase in the Index of Agreement between evaluators for absent, mild, and severe hypernasality, this increase did not reach statistical significance.

CONCLUSION

The auditory-perceptual training provided did not result in a significant improvement in the hypernasality classification for the inexperienced speech-language pathologists, even though the individual data analysis showed that the training favored one of the evaluators. Further studies involving gradual and more extensive auditory-perceptual training may favor the classification of hypernasality by inexperienced SLPs.

Human-Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

With the development of science and technology, human-machine interaction has brought great benefits to the society. Here, we design a voice and gesture signal translator (VGST), which can translate natural actions into electrical signals and realize efficient communication in human-machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response of 20-2000 Hz with a high sensitivity of 167 mV/dB, and the resolution of frequency detection can reach 0.1 Hz. By designing its internal structure, its resonant frequency and output voltage can be adjusted. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function of speech recognition with a high accuracy rate of 97%. It also has good antinoise performance to recognize speech correctly even in noisy environments. Meanwhile, in gesture recognition, the triboelectric translator is able to recognize simple hand gestures and to judge the distance between hand and the VGST based on the principle of electrostatic induction. This work demonstrates that triboelectric nanogenerator (TENG) technology can have great application prospects and significant advantages in human-machine interaction and high-fidelity platforms.