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Dysarthria, a speech disorder often caused by neurological damage, compromises the control of vocal muscles in patients, making their speech unclear and communication troublesome. Recently, voice-driven methods have been proposed to improve the speech intelligibility of patients with dysarthria. However, most methods require a significant representation of both the patient’s and target speaker’s corpus, which is problematic. This study aims to propose a data augmentation-based voice conversion (VC) system to reduce the recording burden on the speaker. We propose dysarthria voice conversion 3.1 (DVC 3.1) based on a data augmentation approach, including text-to-speech and StarGAN-VC architecture, to synthesize a large target and patient-like corpus to lower the burden of recording. An objective evaluation metric of the Google automatic speech recognition (Google ASR) system and a listening test were used to demonstrate the speech intelligibility benefits of DVC 3.1 under free-talk conditions. The DVC system without data augmentation (DVC 3.0) was used for comparison. Subjective and objective evaluation based on the experimental results indicated that the proposed DVC 3.1 system enhanced the Google ASR of two dysarthria patients by approximately [62.4%, 43.3%] and [55.9%, 57.3%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. Further, the proposed DVC 3.1 increased the speech intelligibility of two dysarthria patients by approximately [54.2%, 22.3%] and [63.4%, 70.1%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. The proposed DVC 3.1 system offers significant potential to improve the speech intelligibility performance of patients with dysarthria and enhance verbal communication quality.

Summary: Background: Hearing loss is a common morbidity that requires a hearing device to improve quality of life and prevent sequelae, such as dementia, depression falls, and cardiovascular disease. However, conventional hearing aids have some limitations, including poor accessibility and unaffordability. Consequently, personal sound amplification products (PSAPs) are considered a potential first-line alternative remedy for patients with hearing loss. The main objective of this study was to compare the efficacy of PSAPs and conventional hearing aids regarding hearing benefits in patients with hearing loss. Methods: This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Five databases and reference lists were searched from inception to January 12, 2022. Studies including randomised, controlled trials; nonrandomised, controlled trials; or observational studies comparing PSAPs and hearing aids with regard to hearing gain performance (e.g., speech intelligence) were considered eligible. The review was registered prospectively on PROSPERO (CRD42021267187). Findings: Of 599 records identified in the preliminary search, five studies were included in the review and meta-analysis. A total of 124 patients were divided into the PSAP group and the conventional hearing aid group. Five studies including seven groups compared differences for speech intelligence in the signal-noise ratio (SNR) on the hearing in noise test (HINT) between PSAPs and conventional hearing aids. The pooled results showed nonsignificant differences in speech intelligence (SMD, 0.14; 95% CI, -0.19 to 0.47; P = .41; I2=65%), sound quality (SMD, -0.37; 95% CI, -0.87 to 0.13; P = .15; I2=77%) and listening effort (SMD 0.02; 95% CI, -0.24 to 0.29; P = .86; I2=32%). Nonsignificant results were also observed in subsequent analyses after excluding patients with moderately severe hearing loss. Complete sensitivity analyses with all of the possible combinations suggested nonsignificant results in most of the comparisons between PSAPs and conventional hearing aids. Interpretation: PSAPs are potentially beneficial as conventional hearing aids are in patients with hearing loss. The different features among PSAPs should be considered for patients indicated for hearing devices. Funding: This work was supported by grants from Ministry of Science and Technology (MOST-10-2622-8-075-001) and Veterans General Hospitals and University System of Taiwan Joint Research Program (VGHUST111-G6-11-2 and VGHUST111c-140).

Mechanisms underlying exercise-induced muscle fatigue and recovery are dependent on peripheral changes at the muscle level and improper control of motoneurons by the central nervous system. In this study, we analyzed the effects of muscle fatigue and recovery on the neuromuscular network through the spectral analysis of electroencephalography (EEG) and electromyography (EMG) signals. A total of 20 healthy right-handed volunteers performed an intermittent handgrip fatigue task. In the prefatigue, postfatigue, and postrecovery states, the participants contracted a handgrip dynamometer with sustained 30% maximal voluntary contractions (MVCs); EEG and EMG data were recorded. A considerable decrease was noted in EMG median frequency in the postfatigue state compared with the findings in other states. Furthermore, the EEG power spectral density of the right primary cortex exhibited a prominent increase in the gamma band. Muscle fatigue led to increases in the beta and gamma bands of contralateral and ipsilateral corticomuscular coherence, respectively. Moreover, a decrease was noted in corticocortical coherence between the bilateral primary motor cortices after muscle fatigue. EMG median frequency may serve as an indicator of muscle fatigue and recovery. Coherence analysis revealed that fatigue reduced the functional synchronization among bilateral motor areas but increased that between the cortex and muscle.

BackgroundCochlear implant technology is a well-known approach to help deaf individuals hear speech again and can improve speech intelligibility in quiet conditions; however, it still has room for improvement in noisy conditions. More recently, it has been proven that deep learning–based noise reduction, such as noise classification and deep denoising autoencoder (NC+DDAE), can benefit the intelligibility performance of patients with cochlear implants compared to classical noise reduction algorithms. ObjectiveFollowing the successful implementation of the NC+DDAE model in our previous study, this study aimed to propose an advanced noise reduction system using knowledge transfer technology, called NC+DDAE_T; examine the proposed NC+DDAE_T noise reduction system using objective evaluations and subjective listening tests; and investigate which layer substitution of the knowledge transfer technology in the NC+DDAE_T noise reduction system provides the best outcome. MethodsThe knowledge transfer technology was adopted to reduce the number of parameters of the NC+DDAE_T compared with the NC+DDAE. We investigated which layer should be substituted using short-time objective intelligibility and perceptual evaluation of speech quality scores as well as t-distributed stochastic neighbor embedding to visualize the features in each model layer. Moreover, we enrolled 10 cochlear implant users for listening tests to evaluate the benefits of the newly developed NC+DDAE_T. ResultsThe experimental results showed that substituting the middle layer (ie, the second layer in this study) of the noise-independent DDAE (NI-DDAE) model achieved the best performance gain regarding short-time objective intelligibility and perceptual evaluation of speech quality scores. Therefore, the parameters of layer 3 in the NI-DDAE were chosen to be replaced, thereby establishing the NC+DDAE_T. Both objective and listening test results showed that the proposed NC+DDAE_T noise reduction system achieved similar performances compared with the previous NC+DDAE in several noisy test conditions. However, the proposed NC+DDAE_T only required a quarter of the number of parameters compared to the NC+DDAE. ConclusionsThis study demonstrated that knowledge transfer technology can help reduce the number of parameters in an NC+DDAE while keeping similar performance rates. This suggests that the proposed NC+DDAE_T model may reduce the implementation costs of this noise reduction system and provide more benefits for cochlear implant users.

With the development of active noise cancellation (ANC) technology, ANC has been used to mitigate the effects of environmental noise on audiometric results. However, objective evaluation methods supporting the accuracy of audiometry for ANC exposure to different levels of noise have not been reported. Accordingly, the audio characteristics of three different ANC headphone models were quantified under different noise conditions and the feasibility of ANC in noisy environments was investigated. Steady (pink noise) and non-steady noise (cafeteria babble noise) were used to simulate noisy environments. We compared the integrity of pure-tone signals obtained from three different ANC headphone models after processing under different noise scenarios and analyzed the degree of ANC signal correlation based on the Pearson correlation coefficient compared to pure-tone signals in quiet. The objective signal correlation results were compared with audiometric screening results to confirm the correspondence. Results revealed that ANC helped mitigate the effects of environmental noise on the measured signal and the combined ANC headset model retained the highest signal integrity. The degree of signal correlation was used as a confidence indicator for the accuracy of hearing screening in noise results. It was found that the ANC technique can be further improved for more complex noisy environments.

Abstract Background Despite attempts in retracing the history of the Thao people in Taiwan using folktales, linguistics, physical anthropology, and ethnic studies, their history remains incomplete. The heritage of Thao has been associated with the Pazeh Western plains peoples and several other mountain peoples of Taiwan. In the last 400 years, their culture and genetic profile have been reshaped by East Asian migrants. They were displaced by the Japanese and the construction of a dam and almost faced extinction. In this paper, genetic information from mitochondrial DNA (mtDNA), Histoleucocyte antigens (HLA), and the non-recombining Y chromosome of 30 Thao individuals are compared to 836 other Taiwan Mountain and Plains Aborigines (TwrIP & TwPp), 384 Non-Aboriginal Taiwanese (non-TwA) and 149 Continental East Asians. Results The phylogeographic analyses of mtDNA haplogroups F4b and B4b1a2 indicated gene flow between Thao, Bunun, and Tsou, and suggested a common ancestry from 10,000 to 3000 years ago. A claim of close contact with the heavily Sinicized Pazeh of the plains was not rejected and suggests that the plains and mountain peoples most likely shared the same Austronesian agriculturist gene pool in the Neolithic. Conclusions Having been moving repeatedly since their arrival in Taiwan between 6000 and 4500 years ago, the Thao finally settled in the central mountain range. They represent the last plains people whose strong bonds with their original culture allowed them to preserve their genetic heritage, despite significant gene flow from the mainland of Asia. Representing a considerable contribution to the genealogical history of the Thao people, the findings of this study bear on ongoing anthropological and linguistic debates on their origin.

Nonnegative matrix factorization (NMF) is a useful decomposition technique for multivariate data. More recently, NMF technology was used as a noise-estimation stage for a Wiener-filtering-based noise reduction (NR) method to improve the quality of noisy speech. Previous studies showed that this method provides better sound quality performance than conventional NMF-based approaches; however, there is still scope for improving the performance under noisy listening conditions. More specifically, the performance of an NMF noise estimator for calculating the noise level is considered sensitive to diverse noise environments and signal-to-noise ratio conditions. Therefore, we proposed an adaptive algorithm that derives an adaptive factor ($\alpha $ ) to adjust the weight between the estimated speech and noise levels on the basis of the signal-to-noise level for the gain function of the Wiener-filtering-based NR method to further improve the sound quality. Two objective evaluations and listening tests evaluated the benefits of the proposed method, and experimental results show that better output sound quality and competed for speech intelligibility performance can be achieved when compared with conventional unsupervised NR and NMF-based methods.

Traditional otoscopy has some limitations, including poor visualization and inadequate time for evaluation in suboptimal environments. Smartphone-enabled otoscopy may improve examination quality and serve as a potential diagnostic tool for middle ear diseases using a telemedicine approach. The main objectives are to compare the correctness of smartphone-enabled otoscopy and traditional otoscopy and to evaluate the diagnostic confidence of the examiner via meta-analysis. From inception through 20 January 2022, the Cochrane Library, PubMed, EMBASE, Web of Science, and Scopus databases were searched. Studies comparing smartphone-enabled otoscopy with traditional otoscopy regarding the outcome of interest were eligible. The relative risk (RR) for the rate of correctness in diagnosing ear conditions and the standardized mean difference (SMD) in diagnostic confidence were extracted. Sensitivity analysis and trial sequential analyses (TSAs) were conducted to further examine the pooled results. Study quality was evaluated by using the revised Cochrane risk of bias tool 2. Consequently, a total of 1840 examinees were divided into the smartphone-enabled otoscopy group and the traditional otoscopy group. Overall, the pooled result showed that smartphone-enabled otoscopy was associated with higher correctness than traditional otoscopy (RR, 1.26; 95% CI, 1.06 to 1.51; p = 0.01; I2 = 70.0%). Consistently significant associations were also observed in the analysis after excluding the simulation study (RR, 1.10; 95% CI, 1.00 to 1.21; p = 0.04; I2 = 0%) and normal ear conditions (RR, 1.18; 95% CI, 1.01 to 1.40; p = 0.04; I2 = 65.0%). For the confidence of examiners using both otoscopy methods, the pooled result was nonsignificant between the smartphone-enabled otoscopy and traditional otoscopy groups (SMD, 0.08; 95% CI, -0.24 to 0.40; p = 0.61; I2 = 16.3%). In conclusion, smartphone-enabled otoscopy was associated with a higher rate of correctness in the detection of middle ear diseases, and in patients with otologic complaints, the use of smartphone-enabled otoscopy may be considered. More large-scale studies should be performed to consolidate the results.

Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m/s2) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.

In this paper, we propose a smartphone-based hearing assistive system (termed SmartHear) to facilitate speech recognition for various target users, who could benefit from enhanced listening clarity in the classroom. The SmartHear system consists of transmitter and receiver devices (e.g., smartphone and Bluetooth headset) for voice transmission, and an Android mobile application that controls and connects the different devices via Bluetooth or WiFi technology. The wireless transmission of voice signals between devices overcomes the reverberation and ambient noise effects in the classroom. The main functionalities of SmartHear include: 1) configurable transmitter/receiver assignment, to allow flexible designation of transmitter/receiver roles; 2) advanced noise-reduction techniques; 3) audio recording; and 4) voice-to-text conversion, to give students visual text aid. All the functions are implemented as a mobile application with an easy-to-navigate user interface. Experiments show the effectiveness of the noise-reduction schemes at low signal-to-noise ratios in terms of standard speech perception and quality indices, and show the effectiveness of SmartHear in maintaining voice-to-text conversion accuracy regardless of the distance between the speaker and listener. Future applications of SmartHear are also discussed.

Voice control is an important way of controlling mobile devices; however, using it remains a challenge for dysarthric patients. Currently, there are many approaches, such as automatic speech recognition (ASR) systems, being used to help dysarthric patients control mobile devices. However, the large computation power requirement for the ASR system increases implementation costs. To alleviate this problem, this study proposed a convolution neural network (CNN) with a phonetic posteriorgram (PPG) speech feature system to recognize speech commands, called CNN–PPG; meanwhile, the CNN model with Mel-frequency cepstral coefficient (CNN–MFCC model) and ASR-based systems were used for comparison. The experiment results show that the CNN–PPG system provided 93.49% accuracy, better than the CNN–MFCC (65.67%) and ASR-based systems (89.59%). Additionally, the CNN–PPG used a smaller model size comprising only 54% parameter numbers compared with the ASR-based system; hence, the proposed system could reduce implementation costs for users. These findings suggest that the CNN–PPG system could augment a communication device to help dysarthric patients control the mobile device via speech commands in the future.

Following publication of the original article [1], we have been notified that Additional file 3 was published with track changes.

Temporal envelope is the primary acoustic cue used in most cochlear implant (CI) speech processors to elicit speech perception for patients fitted with CI devices. Envelope compression narrows down envelope dynamic range and accordingly degrades speech understanding abilities of CI users, especially under challenging listening conditions (e.g., in noise). A new adaptive envelope compression (AEC) strategy was proposed recently, which in contrast to the traditional static envelope compression, is effective at enhancing the modulation depth of envelope waveform by making best use of its dynamic range and thus improving the intelligibility of envelope-based speech. The present study further explored the effect of adaptation rate in envelope compression on the intelligibility of compressed-envelope based speech. Moreover, since noise reduction is another essential unit in modern CI systems, the compatibility of AEC and noise reduction was also investigated. In this study, listening experiments were carried out by presenting vocoded sentences to normal hearing listeners for recognition. Experimental results demonstrated that the adaptation rate in envelope compression had a notable effect on the speech intelligibility performance of the AEC strategy. By specifying a suitable adaptation rate, speech intelligibility could be enhanced significantly in noise compared to when using static envelope compression. Moreover, results confirmed that the AEC strategy was suitable for combining with noise reduction to improve the intelligibility of envelope-based speech in noise.

OBJECTIVE: The purpose of this study was to design and to verify a new hearing-aid fitting strategy (Aescu HRL-1) based on the acoustic features of Mandarin. The subjective and objective outcomes were compared to those fitted with NAL-NL1 (National Acoustic Laboratory Non-Linear, version1) in Mandarin-speaking hearing-aid users. DESIGN: Fifteen subjects with sensorineural hearing loss participated in this preliminary study. Each subject wore a pair of four-channel hearing aids fitted with the Aescu HRL-1 and NAL-NL1 prescriptions alternatively for 1 month. Objective and subjective tests including the Mandarin Monosyllable Recognition Test (MMRT), Mandarin Hearing in Noise Test (MHINT), International Outcome Inventory for Hearing Aids (IOI-HA), and a sound-quality questionnaire were used to evaluate the performance of the two prescriptions. RESULTS: The mean MMRT scores were 79.9% and 81.1% for NAL-NL1 and Aescu HRL-1 respectively. They are not statistically different. The corresponding MHINT signal-to-noise ratios were 0.87 and 0.85 dB, also, no significant difference was found between these two strategies. However, in subjective questionnaires, overall, the sound-quality and IOI-HA scores were higher for Aescu HRL-1. CONCLUSIONS: The speech recognition performance based on Aescu HRL-1 is as good as that of NAL-NL1 for Mandarin-speaking hearing-aid users. Moreover, the subjects generally responded that Aescu HRL-1 provides a more natural, richer, and better sound quality than does NAL-NL1.

Conventionally, the maximum likelihood (ML) criterion is applied to train a deep belief network (DBN). We present a maximum entropy (ME) learning algorithm for DBNs, designed specifically to handle limited training data. Maximizing only the entropy of parameters in the DBN allows more effective generalization capability, less bias towards data distributions, and robustness to over-fitting compared to ML learning. Results of text classification and object recognition tasks demonstrate ME-trained DBN outperforms ML-trained DBN when training data is limited.