Articulation Index Research Articles

Executive Function Associations With Audibility-Adjusted Speech Perception in Noise.

Speech recognition in noise is challenging for listeners and appears to require support from executive functions to focus attention on rapidly unfolding target speech, track misunderstanding, and sustain attention. The current study was designed to test the hypothesis that lower executive function abilities explain poorer speech recognition in noise, including among older participants with hearing loss who often exhibit diminished speech recognition in noise and cognitive abilities. A cross-sectional sample of 400 younger-to-older adult participants (19 to < 90 years of age) from the community-based Medical University of South CarolinaLongitudinal Cohort Study of Age-related Hearing Loss were administered tasks with executive control demands to assess individual variability in a card-sorting measure of set-shifting/performance monitoring, a dichotic listening measure of selective attention/working memory, sustained attention, and processing speed. Key word recognition in the high- and low-context speech perception-in-noise (SPIN) tests provided measures of speech recognition in noise. The SPIN scores were adjusted for audibility using the Articulation Index to characterize the impact of varied hearing sensitivity unrelated to reduced audibility on cognitive and speech recognition associations. Set-shifting, dichotic listening, and processing speed each explained unique and significant variance in audibility-adjusted, low-context SPIN scores (ps < .001), including after controlling for age, pure-tone threshold average (PTA), sex, and education level. The dichotic listening and processing speed effect sizes were significantly diminished when controlling for PTA, indicating that participants with poorer hearing sensitivity were also likely to have lower executive function and lower audibility-adjusted speech recognition. Poor set-shifting/performance monitoring, slow processing speed, and poor selective attention/working memory appeared to partially explain difficulties with speech recognition in noise after accounting for audibility. These results are consistent with the premise that distinct executive functions support speech recognition in noise.

Variability in Vowel Space in Parkinson's Disease: Associations With Cognitive and Motor Impairment.

People with Parkinson's disease (PwP) typically experience impairments in vowel articulation; however, less is known about how this measure varies with speech task type and clinical characteristics such as cognitive impairment. We characterized vowel space in PwP with and without mild cognitive impairment (MCI) comparing performance across phonation, reading, and picture description tasks. We evaluated associations between vowel space and cognitive impairment, as well as motor symptom severity to elucidate the factors contributing to variability in this acoustic measure. PwP (n = 48) and age-matched controls (n = 15) performed sustained phonation of corner vowels, a reading passage, and a picture description task (Cookie Theft picture). PwP participants were classified as with normal cognition (PD-NC) or MCI (PD-MCI), and motor symptoms were assessed using the Movement Disorders Society Unified Parkingson's Disease Rating Scale Part III (MDS-UPDRS Part III). Vowel articulation index (VAI) for each task and mean difference in VAI between tasks was compared between the groups using linear mixed models adjusted for age, sex, and education. The impact of motor severity was assessed by additionally adjusting the model for MDS-UPDRS Part III score. In the adjusted mixed model, mean VAI was significantly lower in the PD-MCI group compared to the PD-NC group for all tasks. Within participants, adjusted mean differences demonstrated that all groups declined in VAI when sustained phonation was compared to either reading or picture description tasks. Adjustment for MDS-UPDRS Part III did not alter the results, suggesting no major association of motor impairment with vowel space variability within or between individuals or groups. Variability in vowel space is impacted by cognitive impairment and speech task in PwP. These findings are relevant to the further development of speech markers in PwP and other neurogenerative diseases that impact both cognitive and motor functions.

A study on a micro perforated panel applied luggage board design for road noise reduction

The automotive industry is on the age of rapid transformation from internal combustion engine vehicles to electric vehicles powered by motors. The main source of noise in conventional internal combustion engine vehicles is the engine, while electric vehicles are mainly affected by road and wind noise. A road noise in the medium and low frequency bands, caused by friction between tires and road surfaces, can be improved by increasing the thickness and weight of the insulation material. Still, this countermeasure causes other challenges that related to driving efficiency decrease because of limitations space and weight. As a new method to reduce road noise, therefore, the study introduces the luggage board which applied the micro-perforated panel system. This system utilizes a resonance-type sound absorbing material with excellent sound absorption characteristics at specific frequencies so that a tire pattern noise within the 800-1,000 Hz could be reduced. Because the thickness of the luggage board is partially limited, the study set a number of variations such as changing the porosity, perforated diameter in the perforated panel system. As a result, the study confirmed 2% improvement A.I. (Articulation Index) in the interior with installation of the designed luggage board on the vehicle.

Research on Characteristics of Cab Interior noise under Different Conditions by Neural Network Algorithm

This study aimed at establishing models to predict commercial vehicle's running conditions by neural network algorithms. Initially, experiments were carried out to collect cab interior noise data and a total of 420 samples were obtained after repeated tests. Then, each sample was intercepted and converted into six psychological acoustic metrics, which were sound pressure level (SPL), loudness, sharpness, roughness, articulation index and fluctuation strength. Finally, neural network algorithms were used to establish models between the running condition and the acoustic metrics. Through iterations, AC state, loading state, speed level, and road surfaces prediction models were figured out with accuracies of 0.903, 0.753, 0.978, 0.946, respectively. The results indicate that noise induced by speed variances greatly affects SPL, whereas road surfaces and AC states greatly influence sharpness and articulation index, separately. However, more evidence should be offered to verify the loading states have significant effects on interior noise.

Characterizing acoustic dimensions of health-related urban greenspace

The association of urban greenspace and human health and well-being is widely recognised, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is frequently proposed as a mediator between greenspace and human health. While it is commonly viewed as a negative health factor (e.g. noise pollution), there is growing evidence that it also has positive effects on human health. However, a general problem is the lack of information on the AE for greenspaces in high spatial resolution. To provide evidence-based support for research on this issue, we identify and assess acoustic properties of health-related urban greenspace by estimating the association between urban green area and selected acoustic indices.The analysis is based on 5-minute audio recordings collected systematically from 730 locations, four times a year (n = 2,746) in Bochum, Germany. To quantify the acoustic properties, we use an indicator-set of 30 sound pressure levels, 11 eco-, 78 psychoacoustic indices and 3 complex network measures. Greenspace is defined, in alignment with various health studies, as the percentage of green area within three buffer sizes (50, 100, 300 m) around each recording location.Through cluster analysis, we identify eleven acoustic dimensions in our indicator-set, for which representative indices are derived using principal component analysis. Descriptive statistics and multi-level regression models are used to analyse the associations between representative indices and greenspace. We find the highest effect sizes for Link Density (representing “acoustic dominance”), Acoustic Richness (representing “persistent sound volume”) and the Articulation Index (representing “sound volume”). Thus, in addition to “sound volume”, the AE of urban greenspace is characterised to a similar extent by at least two additional acoustic dimensions. If properties of the AE act as mediators between urban greenspace and human health, these three acoustic dimensions hold the potential to reflect them. Studies on pathways between greenspace and human health could incorporate measures of these dimensions to extend the understanding of the AE’s relevance for Public Health.

Acoustic Effects of Speaker Sex, Speech Sample, and Mandarin Tone on Vowel Production of Poststroke Spastic Dysarthria

Plain Language SummaryMandarin speakers with poststroke spastic dysarthria often have vowel production deficits that affect articulation and speech intelligibility. Vowel production is influenced by many factors. This study explored the acoustic effects of speaker sex, tones, and speech samples and their interactions on vowel production in Mandarin speakers with poststroke spastic dysarthria and confirmed the existence of these effects. We found that regardless of the speech samples or tones, there were the reduced range of articulatory movements, centralized vowel production, and prolonged vowel duration in dysarthria compared to healthy people. The most sensitive and useful analysis measures were the vowel articulation index and vowel duration. Specific influencing factors (speaker sex, speech sample, and tone) of vowel production need to be taken seriously by speech and language pathologists and considered in the assessment and rehabilitation.

Vowel articulation and intelligibility of speech in Spanish speakers with Parkinson's disease treated with deep brain stimulation of the subthalamic nucleus

Parkinson's disease (PD) can affect the intelligibility of speech. Although studies of vowel production are useful for measuring this impairment, they do not exist in Spanish speakers with the disease who have been treated with subthalamic nucleus deep brain stimulation (STN-DBS). STN-DBS is an effective treatment for the classic signs of PD, but with varied effects on speech. Data from two individuals with PD treated with STN-DBS were studied: one (P1) had impairment and the other (P2) had stable speech intelligibility after STN-DBS implantation. The triangular vowel space area (tVSA) and the vowel articulation index (VAI) were measured and compared before the implantation surgery, and at three, six and nine months after surgery. These measurements were compared with measurements of speech intelligibility (percentage of words correctly identified and degree of intelligibility). Both participants presented variations in measurements of vowel articulation after surgery. In P1, the reduction in the tVSA, but not the post-surgical change in the VAI, was consistent with reduced speech intelligibility. However, in P2, both measurements (tVSA and VAI) reflected stable speech intelligibility after surgery. Reduced speech intelligibility in Spanish speakers with PD after STN-DBS implantation may be reflected in a reduced tVSA.

Subclinical articulatory changes of vowel parameters in Korean amyotrophic lateral sclerosis patients with perceptually normal voices.

The available quantitative methods for evaluating bulbar dysfunction in patients with amyotrophic lateral sclerosis (ALS) are limited. We aimed to characterize vowel properties in Korean ALS patients, investigate associations between vowel parameters and clinical features of ALS, and analyze subclinical articulatory changes of vowel parameters in those with perceptually normal voices. Forty-three patients with ALS (27 with dysarthria and 16 without dysarthria) and 20 healthy controls were prospectively collected in the study. Dysarthria was assessed using the ALS Functional Rating Scale-Revised (ALSFRS-R) speech subscores, with any loss of 4 points indicating the presence of dysarthria. The structured speech samples were recorded and analyzed using Praat software. For three corner vowels (/a/, /i/, and /u/), data on the vowel duration, fundamental frequency, frequencies of the first two formants (F1 and F2), harmonics-to-noise ratio, vowel space area (VSA), and vowel articulation index (VAI) were extracted from the speech samples. Corner vowel durations were significantly longer in ALS patients with dysarthria than in healthy controls. The F1 frequency of /a/, F2 frequencies of /i/ and /u/, the VSA, and the VAI showed significant differences between ALS patients with dysarthria and healthy controls. The area under the curve (AUC) was 0.912. The F1 frequency of /a/ and the VSA were the major determinants for differentiating ALS patients who had not yet developed apparent dysarthria from healthy controls (AUC 0.887). In linear regression analyses, as the ALSFRS-R speech subscore decreased, both the VSA and VAI were reduced. In contrast, vowel durations were found to be rather prolonged. The analyses of vowel parameters provided a useful metric correlated with disease severity for detecting subclinical bulbar dysfunction in ALS patients.

A comparative study of automatic vowel articulation index and auditory-perceptual assessments of speech intelligibility in Parkinson’s disease

Purpose The purpose of this study was to analyse the relationship between automatic vowel articulation index (aVAI) and direct magnitude estimation (DME) among speakers with Parkinson’s disease (PD) and healthy controls. We further analysed the potential of aVAI to serve as an objective measure of speech impairment in the clinical setting. Method Speech samples from native Finnish speakers were utilised. Expert raters utilised DME to scale the intelligibility of speech samples. aVAI scores for PD speakers and healthy control speakers were analysed in relationship to DME speech intelligibility ratings and, among PD speakers, disease stage utilising nonparametric statistical analysis. Result Mean DME intelligibility ratings were lower among PD speakers compared to healthy controls. Mean aVAI scores were nearly the same between speaker groups. DME intelligibility ratings and aVAI were strongly correlated within the PD speaker group. aVAI and DME intelligibility ratings were moderately correlated with disease stage as measured by the Hoehn and Yahr scale. Conclusion aVAI was observed to be a promising tool for analysing vowel articulation in PD speakers. Further research is warranted on the application of aVAI as an objective measure of severity of speech impairment in the clinical setting, with varying patient populations and speech samples.

Vowel production: a potential speech biomarker for early detection of dysarthria in Parkinson's disease.

Our aim is to detect early, subclinical speech biomarkers of dysarthria in Parkinson's disease (PD), i.e., systematic atypicalities in speech that remain subtle, are not easily detectible by the clinician, so that the patient is labeled "non-dysarthric." Based on promising exploratory work, we examine here whether vowel articulation, as assessed by three acoustic metrics, can be used as early indicator of speech difficulties associated with Parkinson's disease. This is a prospective case-control study. Sixty-three individuals with PD and 35 without PD (healthy controls-HC) participated in this study. Out of 63 PD patients, 43 had been diagnosed with dysarthria (DPD) and 20 had not (NDPD). Sustained vowels were recorded for each speaker and formant frequencies were measured. The analyses focus on three acoustic metrics: individual vowel triangle areas (tVSA), vowel articulation index (VAI) and the Phi index. tVSA were found to be significantly smaller for DPD speakers than for HC. The VAI showed significant differences between these two groups, indicating greater centralization and lower vowel contrasts in the DPD speakers with dysarhtria. In addition, DPD and NDPD speakers had lower Phi values, indicating a lower organization of their vowel system compared to the HC. Results also showed that the VAI index was the most efficient to distinguish between DPD and NDPD whereas the Phi index was the best acoustic metric to discriminate NDPD and HC. This acoustic study identified potential subclinical vowel-related speech biomarkers of dysarthria in speakers with Parkinson's disease who have not been diagnosed with dysarthria.

The Impact of Sound Pressure Level, Loudness, Roughness, Sharpness, Articulation Index, Hand Vibration, and Seat Vibration on Subjective Comfort Perception of Tractor Drivers

Long-term exposure to high levels of vibration and noise can have detrimental effects on the health of tractor drivers. This study aimed to evaluate the subjective comfort experienced by drivers operating large-horsepower tractors. A total of 10 tractors sourced from 5 different manufacturers were subjected to testing. The assessment encompassed three operational conditions, namely, idle, maximum torque, and rated power. Objective measurements, including A-weighted sound pressure level (A-SPL), loudness, sharpness, roughness, articulation index (AI), hand vibration, and seat vibration, were collected. Additionally, subjective comfort evaluations were carried out using a paired comparison test. To predict the subjective comfort of tractor drivers, a novel prediction model was developed by employing a simulated annealing (SA) algorithm to optimize a backpropagation neural network (BPNN). The model successfully achieved accurate predictions of subjective comfort, yielding a maximum prediction error of 4.4%. The study findings revealed that vibration had a more pronounced impact on driver comfort in environments with lower-amplitude noise, whereas high-decibel noise exerted a masking effect on vibration-induced discomfort. In conclusion, the SA-BPNN model, utilizing A-SPL, loudness, sharpness, roughness, AI, hand vibration, and seat vibration as objective parameters, effectively predicted the subjective comfort of tractor drivers. This discovery holds significant implications for tractor manufacturers, who can employ the model to optimize the design of tractor cabs and enhance driver comfort.

A simulation study on the influence of aircraft panel thickness on the cabin sound quality

Currently, noise control aims to optimise the psychoacoustic characteristics of aircraft interior noise to improve comfort and security during a flight. This study investigates the influence of transmission loss of aircraft panels on the sound quality of the cabin. We established an aircraft cabin acoustic model based on the statistical energy analysis method. The psychoacoustic parameters (i.e., loudness, sharpness, roughness, psychoacoustic annoyance, and articulation index) were simulated with the varying thickness of the aluminium alloy layer. Results showed that loudness and roughness significantly reduced, whereas sharpness increased with increasing thickness. The noise annoyance also declined as the thickness increased. However, the articulation index appeared a sudden drop when the thickness increased.

Estimate the noise effect on automatic speech recognition accuracy for mandarin by an approach associating articulation index

The automatic speech recognition (ASR) technology has become one of the fast-growing engineering technologies. The ASR accuracy is often reduced by environmental interference noise. Many studies attempted to improve the ASR word accuracy in noisy backgrounds, but few were concerned about estimating the influence of noise on the word accuracy of ASR systems. This study investigated the effect of stationary noise on the word accuracy for mandarin of two ASR systems (i.e., the Baidu Cloud and Microsoft Azure). We selected the speech material from an open Mandarin speech database. The interference noise contained pink noise, white noise, random noise with equivalent power spectrum to the average power spectrum of mandarin, band-stop filtered noise and several typical household appliances noise (i.e., hairdryer, range hood, blender and water purifier). The signal-to-noise ratios (SNRs) were set to the range −12–3 dB. The Azure ASR system was less affected by noise and tended to have a higher word accuracy under the same noise condition than the Baidu ASR system. We proposed an approach to associate the articulation index (AI) of interference noise with the speech recognition accuracy of ASR systems. A three-parameter logistic model could be established for a specific ASR system, and each parameter is linearly dependent on the AI of interference noise. The performance of ASR systems under different stationary noise conditions could be quantitatively assessed and compared through the approach. Different models should be established for different versions of the ASR systems through this AI-associating approach since the parameters of one model are only applicable to one particular version of an ASR system.

Multi‐stage attention network for monaural speech enhancement

AbstractAlthough current attention‐based speech enhancement methods have been proven to be capable of significantly improving the noise reduction performance, a bottleneck has arisen in juggling both detailed features and high‐level features: The more attention paid to such performance indicators as speech intelligibility index and articulation index will lead to the loss of subtle features such as syllable continuity and timbre distortion. To tackle such a problem, we divide the speech enhancement model into multiple stages, with a special attention mechanism introduced in each stage so that the detailed speech information is retained and the features of high levels are captured. In this research, a Multi‐Stage Attention Network (MSANet) is implemented in cascade by using three different attention modules: Self Attention, Channel Attention and Spatial Attention. The attention module in each stage needs only to focus on the features of its corresponding stage, thus giving full play to their respective advantages without affecting or damaging the features of other stages, helping decouple the feature extraction process and obtaining the features capable of complementing one another. The comparisons and ablation experiments show that the performance of MSANet is superior to those of current mainstream time domain or time‐frequency domain state‐of‐the‐art methods. Compared to the baseline, PESQ score of their model (3.11) has increased by 5%, CSIG (4.44) has increased by 2.5%, CBAK (3.63) has increased by 2.8% and COVL (3.81) has increased by 3.8%, which demonstrates the potential of MSANet as speech feature extraction backbones. An implementation of the PyTorch version is available here: http://www.msp‐lab.cn:1436/msp/MSANet.

Articulatory undershoot of vowels in isolated REM sleep behavior disorder and early Parkinson’s disease

Imprecise vowels represent a common deficit associated with hypokinetic dysarthria resulting from a reduced articulatory range of motion in Parkinson’s disease (PD). It is not yet unknown whether the vowel articulation impairment is already evident in the prodromal stages of synucleinopathy. We aimed to assess whether vowel articulation abnormalities are present in isolated rapid eye movement sleep behaviour disorder (iRBD) and early-stage PD. A total of 180 male participants, including 60 iRBD, 60 de-novo PD and 60 age-matched healthy controls performed reading of a standardized passage. The first and second formant frequencies of the corner vowels /a/, /i/, and /u/ extracted from predefined words, were utilized to construct articulatory-acoustic measures of Vowel Space Area (VSA) and Vowel Articulation Index (VAI). Compared to controls, VSA was smaller in both iRBD (p = 0.01) and PD (p = 0.001) while VAI was lower only in PD (p = 0.002). iRBD subgroup with abnormal olfactory function had smaller VSA compared to iRBD subgroup with preserved olfactory function (p = 0.02). In PD patients, the extent of bradykinesia and rigidity correlated with VSA (r = −0.33, p = 0.01), while no correlation between axial gait symptoms or tremor and vowel articulation was detected. Vowel articulation impairment represents an early prodromal symptom in the disease process of synucleinopathy. Acoustic assessment of vowel articulation may provide a surrogate marker of synucleinopathy in scenarios where a single robust feature to monitor the dysarthria progression is needed.

A time series analysis model of the relationship between psychoacoustic parameters of urban soundscape spatial sequences and emotional changes.

A listening test was conducted with 32 participants to obtain data on emotional changes in response to three types of urban soundscape spatial sequences. By establishing a time series model, the relationship between psychoacoustic parameters of the sequence and changes in the two dimensions of emotion was determined. Results showed that psychoacoustic parameters can explain 44% and 40%-49% of the changes in the pleasantness and arousal dimensions of emotion, respectively. Roughness and fluctuation have the highest correlation with emotional changes, while loudness and articulation index have the lowest correlation with emotional changes. This research verified the lags between psychoacoustic changes in the soundscape and the associated perceived emotion. First, there was a 3-4 s lag between psychoacoustic parameters and emotional changes. Second, changes in roughness and loudness could cause synchronous changes in emotions, while other parameters could cause delayed changes in emotions. Finally, the lag of emotion had a strong and stable explanatory power for emotional changes. This research proves the effectiveness of the time series analysis technology in establishing the dynamic relationship between the acoustic parameters of soundscape sequences and the second-by-second perceived emotions and provides a new data analysis method for in-depth study of soundscape sequence perception.

Sound quality modelling of hairdryer noise

The demand for acoustic comfort in domestic appliances increases with the rapid growth of living quality requirements. As a common domestic appliance, the hairdryer produces unwanted noise that affects comfort during working. Manufacturers make great efforts to control the hairdryer noise, e.g., reduce the sound pressure level (SPL) by increasing blades number to make the frequency of noise energy above the hearing range. This study investigated the noise discomfort of a supersonic hairdryer (SHD) and three standard hairdryers (HD). We measured and recorded hairdryer noise at all operating conditions, and generated 120 test stimuli at SPLs ranging from 60 to 72 dB(A) in 3 dB steps based on 24 sound samples. Twenty-four subjects evaluated the noise discomfort of all stimuli using the absolute magnitude estimation (AME) method. The contribution of psychoacoustic metrics, i.e. loudness, sharpness, roughness, fluctuation strength, tonality (hearing model) and articulation index, to the noise discomfort was analysed by partial correlation analysis. Hairdryer noise discomfort was dominated by loudness and significantly influenced by tonality. The sound quality models were established for SHD and HD by multiple linear regression between the discomfort and two psychoacoustic metrics, i.e., loudness and tonality. The SHD performed better in sound quality for less discomfort and interference of communication than the HD, yet the adverse effects of the ultra-high frequencies on comfort and health need further consideration.

Predicting neural deficits in sensorineural hearing loss from word recognition scores

The current gold standard of clinical hearing assessment includes a pure-tone audiogram combined with a word recognition task. This retrospective study tests the hypothesis that deficits in word recognition that cannot be explained by loss in audibility or cognition may reflect underlying cochlear nerve degeneration (CND). We collected the audiological data of nearly 96,000 ears from patients with normal hearing, conductive hearing loss (CHL) and a variety of sensorineural etiologies including (1) age-related hearing loss (ARHL); (2) neuropathy related to vestibular schwannoma or neurofibromatosis of type 2; (3) Ménière’s disease; (4) sudden sensorineural hearing loss (SSNHL), (5) exposure to ototoxic drugs (carboplatin and/or cisplatin, vancomycin or gentamicin) or (6) noise damage including those with a 4-kHz “noise notch” or reporting occupational or recreational noise exposure. Word recognition was scored using CID W-22 monosyllabic word lists. The Articulation Index was used to predict the speech intelligibility curve using a transfer function for CID W-22. The level at which maximal intelligibility was predicted was used as presentation level (70 dB HL minimum). Word scores decreased dramatically with age and thresholds in all groups with SNHL etiologies, but relatively little in the conductive hearing loss group. Discrepancies between measured and predicted word scores were largest in patients with neuropathy, Ménière’s disease and SSNHL, intermediate in the noise-damage and ototoxic drug groups, and smallest in the ARHL group. In the CHL group, the measured and predicted word scores were very similar. Since word-score predictions assume that audiometric losses can be compensated by increasing stimulus level, their accuracy in predicting word score for CHL patients is unsurprising. The lack of a strong age effect on word scores in CHL shows that cognitive decline is not a major factor in this test. Amongst the possible contributions to word score discrepancies, CND is a prime candidate: it should worsen intelligibility without affecting thresholds and has been documented in human temporal bones with SNHL. Comparing the audiological trends observed here with the existing histopathological literature supports the notion that word score discrepancies may be a useful CND metric.

Comparing testing methodologies of speech privacy class in closed offices

Speech privacy is a principal research area in speech communication. The term "speech privacy" is generally interpreted a condition where speech cannot be readily understood by, but may be audible to, an unintended listener. The need to prevent sound from intruding into adjacent spaces in both closed and open-plan settings is a concern in various office buildings. Speech privacy is essentially a function of the signal-to-noise ratio, comprising the noise reduction between source and receiver positions, and the masking effective of background noise. Speech Privacy Class (SPC) is one of the commonly used metrics for speech privacy assessment in closed plan offices in North America. However, there is a reluctance to use SPC between closed rooms due to laborious testing requirements as per ASTM E2638; there is a supposed preference to use either Speech Privacy Potential (SPP) based on simpler NIC testing, or to use Articulation Index (AI) despite the AI being created solely for testing open-plan settings. The ASTM E2638-10 standard SPC testing procedure does not assume a diffuse field in the receiving space but rather evaluates the performance at each potential eavesdropping location on the basis that there may be an intentional listener. Thus, to better apply the SPC to unintended listening and speech privacy in typical commercial spaces, the goal is to compare the current methodology as per ASTM E2638-10 with alternative sampling methods considering the talker location and unintended listening positions outside the source room.

Comparison of performance with hearing aid programmed to NAL-NL1 first-fit and optimized-fit.

The initial-fit provided by the hearing aid manufacturer's software is generally a display of measurement done in the ear simulators. The need for verification of hearing aid output and gain in the real ear using probe-microphone measurement to match the prescriptive target is highlighted. The objective of the study was to evaluate the difference in real-ear aided response (REAR), real-ear insertion gain (REIG), aided thresholds, articulation index (AI) and word recognition score (WRS) in quiet, with hearing aid programmed to NAL-NL1 first-fit and NAL-NL1 optimized-fit using the probe-microphone technique. In a repeated measure experimental design, 11 participants with a mean age of 41.09 (SD=±9.95) years having moderate and moderately-severe sensorineural hearing loss were tested monaurally in two aided conditions, with a 16-channel hearing aid programmed for manufacturer's NAL-NL1 first-fit and optimized-fit to NAL-NL1 using probe-microphone verification. The REAR, REIG, aided threshold, articulation index and word recognition scores in quiet were obtained for both aided conditions. The REAR, REIG, aided threshold, AI and WRS in quiet were significantly better with the NAL-NL1 optimized-fit compared to manufacturer's NAL-NL1 first-fit. The optimized-fit yields better audibility and improved word recognition in quiet. This supports best practice guidelines of many professional organizations regarding the use of probe-microphone measurement as the "Gold standard" for verification of hearing aid fitting, thereby providing better satisfaction and quality of life to hearing aid users.