Sensorineural hearing-impaired listeners suffer severely from deterioration in the processing and internal representation of acoustic signals. In order to understand this deterioration in detail, a numerical perception model was developed which is based on current functional models of the signal processing in the auditory system. To test this model, the individual’s speech intelligibility in quiet and in noise was predicted. The primary input parameter of the model is the precisely measured audiogram of each listener. In a refined version of the model, additional input parameters are derived from predicting the individual’s temporal forward masking and notched-noise measurements with the same model assumptions. The predictions of the perception model were compared with those of the articulation index (AI) and the speech transmission index (STI). The accuracy of prediction with the perception model is in the same range as with the AI and the STI. The model does not require a calibration function and has the advantage of a greater flexibility in including different processing deficits associated with hearing impairment. However, it requires more time for computation. For the hearing-impaired listeners examined so far the individually measured psychoacoustical parameters have only a secondary effect on the prediction as compared to the audiogram. Nevertheless, the underlying model is a first step toward a quantitative understanding of speech intelligibility and helps to distinguish between the influence of the ‘‘attenuation’’ and the ‘‘distortion’’ component of the hearing loss.
Speech-recognition tests are widely used in both clinical and research audiology. The purpose of this study was the development of a novel speech-recognition test that combines concepts of different speech-recognition tests to reduce training effects and allows for a large set of speech material. The new test consists of four different words per trial in a meaningful construct with a fixed structure, the so-called phrases. Various free databases were used to select the words and to determine their frequency. Highly frequent nouns were grouped into thematic categories and combined with related adjectives and infinitives. After discarding inappropriate and unnatural combinations, and eliminating duplications of (sub-)phrases, a total number of 772 phrases remained. Subsequently, the phrases were synthesized using a text-to-speech system. The synthesis significantly reduces the effort compared to recordings with a real speaker. After excluding outliers, measured speech-recognition scores for the phrases with 31 normal-hearing participants at fixed signal-to-noise ratios (SNR) revealed speech-recognition thresholds (SRT) for each phrase varying up to 4 dB. The median SRT was -9.1 dB SNR and thus comparable to existing sentence tests. The psychometric function's slope of 15 percentage points per dB is also comparable and enables efficient use in audiology. Summarizing, the principle of creating speech material in a modular system has many potential applications.
A multi-talker paradigm is introduced that uses different attentional processes to adjust speech-recognition scores with the goal of conducting measurements at high signal-to-noise ratios (SNR). The basic idea is to simulate a group conversation with three talkers. Talkers alternately speak sentences of the German matrix test OLSA. Each time a sentence begins with the name “Kerstin” (call sign), the participant is addressed and instructed to repeat the last words of all sentences from that talker, until another talker begins a sentence with “Kerstin”. The alternation of the talkers is implemented with an adjustable overlap time that causes an overlap between the call sign “Kerstin” and the target words to be repeated. Thus, the two tasks of detecting “Kerstin” and repeating target words are to be done at the same time. The paradigm was tested with 22 young normal-hearing participants (YNH) for three overlap times (0.6 s, 0.8 s, 1.0 s). Results for these overlap times show significant differences, with median target word recognition scores of 88%, 82%, and 77%, respectively (including call-sign and dual-task effects). A comparison of the dual task with the corresponding single tasks suggests that the observed effects reflect an increased cognitive load.
Hearing impairment is associated with a decrease in speech intelligibility and health-related quality of life, such as social isolation and participation restriction. However, little is known about the extent to which hearing impairment and hearing aid fittings change behavior in acute communication situations as well as interrelated behavior patterns. Based on a pilot study, in which the basis for annotating communication behavior was laid, group discussions in noise were initiated with 10 participants using three different hearing-aid brands. The proposed offline annotation scheme revealed that different hearing aids were associated with changes in behavior patterns. These behavioral changes were congruent with speech recognition threshold results and also with subjective assessments. Some of the results were interpreted in terms of participation restriction and activity limitation following the framework of the International Classification of Functioning, Disability and Health. In addition to the offline annotation scheme, a procedure for instantaneous coding of eight behavior patterns was iteratively developed and used for the quick examination of lab studies with good to excellent interrater reliability values.
Patients undergo comprehensive diagnostics during the preliminary cochlear implant (CI) examination in order to make a decision for or against a cochlear implantation. The medical indication as well as the patient's wish is examined by an interdisciplinary team by means of various measuring procedures and detailed explanatory discussions. Nevertheless, some patients refuse a cochlear implantation despite a positive indication.
Recently, exploring acoustic conditions of people in their everyday environments has drawn a lot of attention. One of the most important and disturbing sound sources is the test participant’s own voice. This contribution proposes an algorithm to determine the own-voice audio segments (OVS) for blocks of 125 ms and a method for measuring sound pressure levels (SPL) without violating privacy laws. The own voice detection (OVD) algorithm here developed is based on a machine learning algorithm and a set of acoustic features that do not allow for speech reconstruction. A manually labeled real-world recording of one full day showed reliable and robust detection results. Moreover, the OVD algorithm was applied to 13 near-ear recordings of hearing-impaired participants in an ecological momentary assessment (EMA) study. The analysis shows that the grand mean percentage of predicted OVS during one day was approx. 10% which corresponds well to other published data. These OVS had a small impact on the median SPL over all data. However, for short analysis intervals, significant differences up to 30 dB occurred in the measured SPL, depending on the proportion of OVS and the SPL of the background noise.
Objective The aim of this study was to predict outcomes of the HHI questionnaire (Hearing Handicap Inventory) using individual variables beyond pure-tone hearing thresholds.Design An extensive health-related test battery was applied including a general anamnesis, questionnaires, audiological measures, examination of visual acuity, balance, and cognition, as well as tactile- and motor skills. Based on the self-assessment of health variables and different sensory and cognitive performance measures, a frailty index was calculated to describe the health status of the participants. A stepwise linear regression analysis was conducted to predict HHI scores.Study sample A mixed sample (N = 212) of 55- to 81-year-old, participants with different hearing and aiding status completed the test battery.Results The regression analysis showed statistically significant contributions of pure-tone hearing thresholds, speech recognition in noise, age, frailty, mental health, and the willingness to use hearing aids on HHIE outcomes.Conclusions Self-reported hearing handicap assessed with the HHI questionnaire reflects various individual variables additionally to pure-tone hearing loss and speech recognition in noise. It is necessary to be aware of the influences of age and health-related variables on HHI scores when using it in research as well as in clinical settings.
This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study. The analysis method has been implemented as a freely available Python package EmaCalc, RRID:SCR 022943. The analysis model can use EMA input data including nominal categories in one or more situation dimensions, and ordinal ratings of several perceptual attributes. The analysis uses a variant of ordinal regression to estimate the statistical relation between these variables. The Bayesian method has no requirements related to the number of participants or the number of assessments by each participant. Instead, the method automatically includes measures of the statistical credibility of all analysis results, for the given amount of data. For the previously collected EMA data, the analysis results demonstrate how the new tool can handle heavily skewed, scarce, and clustered data that were collected on ordinal scales, and present results on interval scales. The new method revealed results for the population mean that were similar to those obtained in the previous analysis by an advanced regression model. The Bayesian approach automatically estimated the inter-individual variability in the population, based on the study sample, and could show some statistically credible intervention results also for an unseen random individual in the population. Such results may be interesting, for example, if the EMA methodology is used by a hearing-aid manufacturer in a study to predict the success of a new signal-processing method among future potential customers.
