43,347 research outputs found

    The effectiveness of traditional methods and altered auditory feedback in improving speech rate and intelligibility in speakers with Parkinson's disease

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    Communication problems are a frequent symptom for people with Parkinson's disease (PD) which can have a significant impact on their quality-of-life. Deciding on the right management approach can be problematic though, as, with the exception of LSVT (R), very few studies have been published demonstrating the effectiveness of treatment techniques. The aim of this study was to compare traditional rate reduction methods with altered auditory feedback (AAF) with respect to their effectiveness to reduce speech rate and improve intelligibility in speakers with PD. Ten participants underwent both types of treatments in once weekly sessions for 6 weeks. Outcomes measures were speech rate for passage reading as well as intelligibility on both a passage reading and a monologue task. The results showed that, as a group, there was no significant change in either speech rate or intelligibility resulting from either treatment type. However, individual speakers showed improvements in speech performance as a result of each therapy technique. In most cases, these benefits persisted for at least 6 months post-treatment. Possible reasons for the variable response to treatment, as well as issues to consider when planning to use AAF devices in treatment are discussed

    Hearing Loss and the Voice

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    The voice varies according to the context of speech and to the physical and psychological conditions of the human being, and there is always a normal standard for the vocal output. Hearing loss can impair voce production, causing social, educational, and speech limitations, with specific deviation of the communication related to speech and voice. Usually, the voice is not the main focus of the speech-language pathology therapy with individuals with hearing loss, but its deviations can represent such a negative impact on this population that it can interfere on speech intelligibility and crucially compromise the social integration of the individual. The literature vastly explores acoustic and perceptual characteristics of children and adults with hearing loss. Voice problems in individuals with this impairment are directly related to its type and severity, age, gender, and type of hearing device used. While individuals with mild and moderate hearing loss can only present problems with resonance, severely impaired individuals may lack intensity and frequency control, among other alterations. The commonly found vocal deviations include strain, breathiness, roughness, monotone, absence of rhythm, unpleasant quality, hoarseness, vocal fatigue, high pitch, reduced volume, loudness with excessive variation, unbalanced resonance, altered breathing pattern, brusque vocal attack, and imprecise articulation. These characteristics are justified by the incapability of the deaf to control their vocal performance due to the lack of auditory monitoring of their own voice, caused by the hearing loss. Hence, the development of an intelligible speech with a good quality of voice on the hearing impaired is a challenge, despite the sophisticated technological advances of hearing aids, cochlear implants and other implantable devices. The purpose of this chapter is therefore to present an extensive review of the literature and describe our experience regarding the evaluation, diagnosis, and treatment of voice disorders in individuals with hearing loss

    Howling and Entrainment in Hearing Aids: A Review

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    This review focuses on howling and entrainment artifacts in digital hearing aids. The howling may occur (especially at high gains), essentially due to the close proximity of the input microphone and the output loudspeaker. The entrainment, on the other hand, occurs when the input to the hearing aids is periodic, for example, music signals or alarm signals with strong tonal characteristics. We give details on methods for howling avoidance, which are mainly based on adaptive filtering-based acoustic feedback cancellation. We also give an overview of many recent works on entrainment in hearing aids. Finally, we remark that efficient acoustic feedback cancellation scheme which can avoid howling, can also well manage the entrainment artifact

    Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids

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    Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and dereverberation for assisting hearing-impaired listeners. For noise reduction, the performance of the deep learning model was evaluated objectively and compared with that of open Master Hearing Aid (openMHA), a conventional signal processing based framework, and a Deep Neural Network (DNN) based model. It was found that the RNN model can suppress noise and improve speech understanding better than the conventional hearing aid noise reduction algorithm and the DNN model. The same RNN model was shown to reduce reverberation components with proper training. A real-time implementation of the deep learning model is also discussed

    Control of feedback for assistive listening devices

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    Acoustic feedback refers to the undesired acoustic coupling between the loudspeaker and microphone in hearing aids. This feedback channel poses limitations to the normal operation of hearing aids under varying acoustic scenarios. This work makes contributions to improve the performance of adaptive feedback cancellation techniques and speech quality in hearing aids. For this purpose a two microphone approach is proposed and analysed; and probe signal injection methods are also investigated and improved upon

    Receiver position and acceptance of noise, speech understanding, and sound quality ratings

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    The effect of receiver position in a hearing aid on acceptance of background noise, speech intelligibility, sound quality judgments, and listener preference was measured in adults with normal to mild sloping to moderate to severe sensorineural hearing loss. Participants were fit with open-fit behind-the-ear (BTE) and receiver-in-the-ear (RITE) hearing aids. After a 3-week trial with each device, acceptance of noise levels, speech understanding in quiet and in noise, and sound quality ratings were conducted. At the conclusion of the study, listener preference between the devices was evaluated. Results revealed that receiver position did not significantly affect acceptance of background noise, speech understanding in quiet or in noise, sound quality ratings, or listener preference, indicating that no difference in objective or subjective benefit was observed based on the position of the receiver in a BTE hearing aid

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

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    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
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