Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

Accurate speech quality measures are highly attractive and beneficial in the design, fine-tuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications

Electroacoustic assessment of wireless remote microphone systems

Wireless remote microphones (RMs) transmit the desired acoustic signal to the hearing aid (HA) and facilitate enhanced listening in challenging environments. Fitting and verification of RMs, and benchmarking the relative performance of different RM devices in varied acoustic environments are of significant interest to Audiologists and RM developers. This paper investigates the application of instrumental speech intelligibility and quality metrics for characterizing the RM performance in two acoustic environments with varying amounts of background noise and reverberation. In both environments, two head and torso simulators (HATS) were placed 2 m apart, where one HATS served as the talker and the other served as the listener. Four RM systems were interfaced separately with a HA programmed to match the prescriptive targets for the N4 standard audiogram and placed on the listener HATS. The HA output in varied acoustic conditions was recorded and analyzed offline through computational models predicting speech intelligibility and quality. Results showed performance differences among the four RMs in the presence of noise and/or reverberation, with one RM exhibiting significantly better performance. Clinical implications and applications of these results are discussed

Adaptation and implementation of clinical guidelines on maternal and newborn postnatal care in Iran: study protocol

Abstract Background According to World Health Organization (WHO), the postnatal care provision aims to provide care and treatment with the highest quality and the least intervention to obtain the best health and well-being for the family. The present study aims to adapt international guidelines for the clinical recommendations for the postpartum period and implement and determine its effectiveness. Methods/design This study will be done in two phases. In the first phase, international clinical guidelines for mother and newborn postnatal care will be adapted. The second phase is a randomized controlled trial in which the adapted guideline recommendations will be implemented, and maternal and neonatal outcomes will be measured. The ADAPTE method for adaptation of clinical guidelines, is usedg in the first phase. A systematic review was conducted in the databases and clinical guidelines related to postpartum care were extracted according to the inclusion criteria. The quality of clinical guidelines was evaluated using the AGREE-II tool. The WHO clinical guideline obtained the highest evaluation score and was chosen as the main guideline, and the NICE clinical guideline, with a second higher evaluation score, was also used to fill some gaps in the WHO guideline. Based on the pre-determined questions, recommendations will be sent to the relevant experts and stakeholders for their evaluation. After the external evaluation and the finalization of the recommendations, the postpartum clinical guideline will be compiled and used in the second phase of the study. In the second phase, 272 women in the immediate postnatal stage of the maternity and postpartum ward of Taleghani and AL-Zahra Hospitals in Tabriz will be assigned into the intervention (receiving care based on adapted guidline recommendations) and control (receiving routine hospital care) groups uing individual stratified block randomization. At 6 weeks after birth, we will complete the Edinburgh postnatal depression scale, postpartum specific anxiety scale and Barkin index of maternal functioning (to assess the primary outcomes), as well as a maternal health problems checklist, infant care behavior, and violence assessment questionnaires (to asses the seconadary outcomes). Further, the maternal health problems checklist and the Edinburgh postnatal depression scale will be completed in the second week after birth. The data will be analyzed using an independent t-test and ANCOVA. Discussion It is expected that the implementation of evidence-based clinical guidelines improves maternal and neonatal outcomes and experience of the postpartum period. The positive experience can also help to achieve Iran’s population policies and the need to increase childbearing in the country. Trial registration: Iranian Registry of Clinical Trials (IRCT): IRCT20120718010324N76; Date of registration: 27/1/2023. URL: https://en.irct.ir/user/trial/66874/view ; Date of first registration: 27/3/2023