Detection and assessment of hypernasality in repaired cleft palate speech using vocal tract and residual features

Automatic Detection System for Velopharyngeal Insufficiency Based on Acoustic Signals from Nasal and Oral Channels

Velopharyngeal insufficiency (VPI) is a type of pharyngeal function dysfunction that causes speech impairment and swallowing disorder. Speech therapists play a key role on the diagnosis and treatment of speech disorders. However, there is a worldwide shortage of experienced speech therapists. Artificial intelligence-based computer-aided diagnosing technology could be a solution for this. This paper proposes an automatic system for VPI detection at the subject level. It is a non-invasive and convenient approach for VPI diagnosis. Based on the principle of impaired articulation of VPI patients, nasal- and oral-channel acoustic signals are collected as raw data. The system integrates the symptom discriminant results at the phoneme level. For consonants, relative prominent frequency description and relative frequency distribution features are proposed to discriminate nasal air emission caused by VPI. For hypernasality-sensitive vowels, a cross-attention residual Siamese network (CARS-Net) is proposed to perform automatic VPI/non-VPI classification at the phoneme level. CARS-Net embeds a cross-attention module between the two branches to improve the VPI/non-VPI classification model for vowels. We validate the proposed system on a self-built dataset, and the accuracy reaches 98.52%. This provides possibilities for implementing automatic VPI diagnosis.

Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate-A Systematic Review

OBJECTIVE

The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved.

MATERIALS AND METHODS

An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied.

RESULTS

Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis.

CONCLUSIONS

Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

AI Recognition Method of Pronunciation Errors in Oral English Speech with the Help of Big Data for Personalized Learning

At present, there is a lack of careful consideration in the judgment process of pronunciation errors in many English speeches. These pronunciation errors will create a great impact on personalized learning. The process of creating a data set for errors is also not an easy work. On considering the above obstacle, an artificial intelligent recognition method of pronunciation errors in English speeches for personalized learning along with big data is proposed. This method takes the average pronunciation level of standard speech as the basis of pronunciation error judgment, and judges the pronunciation and application of words such as speed, pronunciation, semantics, etc. In the Hidden Markov Model (HMM) modelling method of speech recognition, Viterbi algorithm and improved posterior probability algorithm are implemented to recognize student’s vocalization instinctively. Through the segmentation and scoring of basic units, English learners are provided with reliable pronunciation information feedback, correct pronunciation errors and give corresponding feedback according to the judgment results. The innovation outcome establishes that the intelligent recognition method for personalized learning can efficiently diminish the error rate and enhance the accuracy of error detection. Let the artificial intelligence (AI) correct English learner’s pronunciation errors intelligently.

Evaluation of noise excitation as a method for detection of hypernasality

Hypernasality is a disorder where excess nasal resonance is perceived during speech, often as a result of abnormal coupling between the oral and nasal tracts known as velopharyngeal insufficiency (VPI). The most common cause of VPI is a cleft palate, which affects around 1 in 1650 babies, around ⅓ of whom have persistent speech problems after surgery. Current equipment-based assessment methods are invasive and require expert knowledge, and perceptual assessment methods are limited by the availability of expert listeners and differing interpretations of assessment scales. Spectral analysis of hypernasality within the academic community has resulted in potentially useful spectral indicators, but these are highly variable, vowel specific, and not commonly used within clinical practice. Previous works by others have developed noise excitation technologies for the measurement of oral tract transfer functions using resonance measurement devices (RMD). These techniques provide an opportunity to investigate the structural system abnormalities which lead to hypernasality, without the need for invasive measurement equipment. Thus, the work presented in this study adapts these techniques for the detection of hypernasality. These adaptations include augmentation of the hardware and development of the software, so as to be suitable for transfer function measurement at the nostrils rather than the mouth (nRMD). The new method was tested with a single participant trained in hypernasal production, producing 'normal' and hypernasal vowels, and the recordings validated through a listening test by an expert listener and calculation of nasalance values using a nasality microphone. These validation stages indicated the reliability of the captured data, and analysis of the nRMD measurements indicated the presence of a systematic difference in the frequency range 2 to 2.5 kHz between normal and hypernasal speech. Further investigation is warranted to determine the generalisability of these findings across speakers, and to investigate the origins of differences manifesting in the transfer functions between conditions. This will provide new insights into the effects of nasal tract coupling on voice acoustics, which could in turn lead to the development of useful new tools to support clinicians in their work with hypernasality.

Subjective and Objective Evaluation of Speech in Adult Patients with Unrepaired Cleft Palate

OBJECTIVE

To explore the speech outcomes of adult patients through subjective perception evaluation and objective acoustic analysis, and to compare the differences in pronunciation characteristics between speakers with adult patients with unrepaired cleft palate and their non-cleft peers.

PARTICIPANTS AND INTERVENTION

Subjective evaluation indicators included speech intelligibility, nasality, and consonant missing rate, whereas objective acoustic parameters included normalized vowel formants, voice onset time, and the analysis of three-dimensional spectrogram and spectrum, were carried out on speech samples produced by 2 groups of speakers: (a) speakers with unrepaired cleft palate (n = 65, mean age = 25.1 years) and (b) typical speakers (n = 30, mean age = 23.7 years).

RESULTS

Compared with typical speakers, individuals with unrepaired cleft palate exhibited a lower speech intelligibility with higher nasality and consonant missing rate, the missing rate is highest for the 6 consonants syllables. The acoustic parameters are mainly manifested as differences in vowel formants and voice onset time.

CONCLUSIONS

The results revealed important acoustical differences between adult patients with unrepaired cleft palate and typical speakers. The trend of spectral deviation may have contributed to the difficulty in producing pressure vowels and aspirated consonants in individuals with speech disorders related to cleft palate.

Exploration of the Influences of Temporary Velum Paralysis on Auditory-Perceptual, Acoustic, and Tomographical Markers

Purpose To better understand hypernasality (HN), we explored the relations between velopharyngeal orifice, auditory perception of HN, and acoustic-spectral measures in an in vivo within-subject design: (a) with a normally functioning velum as the control condition and (b) with a temporarily paralyzed velum as the experimental condition. Method The velum of eight volunteers was injected with ropivacaine hydrochloride (Naropin) in the area of the levator veli palatini and tensor veli palatini muscles to induce temporary velopharyngeal inadequacy (VPI) and HN. Sustained [a] and [i] and oronasal text readings were recorded, and 3D cone-beam computed tomography images of the vocal tract were built before and during velar anesthesia. Differences between conditions and correlations in normal-to-numb differences between velopharyngeal cross-sectional area (VP_area), mean ratings of HN severity, and nine acoustic-spectral measures were determined. Results Three subjects already had some incomplete velopharyngeal closure in the control condition. Temporary motor nerve blockage of the velum (increased VP_area) was accomplished in seven subjects, leading to increased HN and changes in three acoustic-spectral measures. Furthermore, significant correlations only emerged between VP_area, HN, and Model_Kataoka. Conclusions In most of the participants, it was possible to temporarily increase the velopharyngeal orifice to investigate HN while controlling other speech variables and cephalic morphology. Although this study was exploratory and its are findings preliminary, it provided additional evidence for the possible clinical value of Model_Kataoka, A ₃-P ₀, and B _F1 for the objective measurement of VPI or HN.

A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children With Cleft Palate

OBJECTIVES

Evaluation of hypernasality requires extensive perceptual training by clinicians and extending this training on a large scale internationally is untenable; this compounds the health disparities that already exist among children with cleft. In this work, we present the objective hypernasality measure (OHM), a speech-based algorithm that automatically measures hypernasality in speech, and validate it relative to a group of trained clinicians.

METHODS

We trained a deep neural network (DNN) on approximately 100 hours of a publicly-available healthy speech corpus to detect the presence of nasal acoustic cues generated through the production of nasal consonants and nasalized phonemes in speech. Importantly, this model does not require any clinical data for training. The posterior probabilities of the deep learning model were aggregated at the sentence and speaker-levels to compute the OHM.

RESULTS

The results showed that the OHM was significantly correlated with perceptual hypernasality ratings from the Americleft database (r = 0.797, p < 0.001) and the New Mexico Cleft Palate Center (NMCPC) database (r = 0.713, p < 0.001). In addition, we evaluated the relationship between the OHM and articulation errors; the sensitivity of the OHM in detecting the presence of very mild hypernasality; and established the internal reliability of the metric. Further, the performance of the OHM was compared with a DNN regression algorithm directly trained on the hypernasal speech samples.

SIGNIFICANCE

The results indicate that the OHM is able to measure the severity of hypernasality on par with Americleft-trained clinicians on thisdataset.