Frequency importance functions for a feature recognition test material

Duggirala, Vasanta; Studebaker, Gerald A.; Pavlovic, Chaslav V.; Sherbecoe, Robert L.

doi:10.1121/1.396316

Cited by 32 publications

(19 citation statements)

References 22 publications

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“…Behaviorally, the crossover frequency has been shown to separate the frequency scale into two portions that are equally important for speech perception, with half of the information above and half below this frequency (French & Steinberg, 1947;Miller & Nicely, 1955;Pavlovic, 1994;Puggirala, et al, 1988). That is, the crossover frequency allows for the evaluation of the relative contribution of different portions of the frequency spectrum to speech recognition.…”

Section: The Effects Of Hearing Loss On Speech-evoked Erpsmentioning

confidence: 97%

Speech Evoked Potentials: From the Laboratory to the Clinic

2008

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Speech-evoked auditory event-related potentials (ERPs) provide insight into the neural mechanisms underlying speech processing. For this reason, ERPs are of great value to hearing scientists and audiologists. This article will provide an overview of ERPs frequently used to examine the processing of speech and other sound stimuli. These ERPs include the P1-N1-P2 complex, acoustic change complex, mismatch negativity, and P3 responses. In addition, we focus on the application of these speech-evoked potentials for the assessment of (1) the effects of hearing loss on the neural encoding of speech allowing for behavioral detection and discrimination; (2) improvements in the neural processing of speech with amplification (hearing aids, cochlear implants); and (3) the impact of auditory training on the neural processing of speech. Studies in these three areas are reviewed and implications for audiologists are discussed.

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Section: The Effects Of Hearing Loss On Speech-evoked Erpsmentioning

confidence: 97%

Speech Evoked Potentials: From the Laboratory to the Clinic

2008

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“…Frequencyimportance functions numerically quantify the importance of each frequency band to the overall speech recognition score for a specific stimulus. These functions have been derived from adult listeners for a wide range of varying speech stimuli, including continuous discourse (Studebaker et al, 1987), high-and low-context sentences (Bell et al, 1992), monosyllabic word lists such as CID W-22 (Studebaker and Sherbecoe, 1991), NU-6 (Studebaker et al, 1993), and nonsense words (Duggirala et al, 1988). Overall, these studies reveal that for stimuli with redundant linguistic content such as familiar words or sentences, importance weights for adult listeners are shifted towards the mid-frequencies and reduced at higher frequencies.…”

Section: Introductionmentioning

confidence: 97%

Audibility-based predictions of speech recognition for children and adults with normal hearing

McCreery

Stelmachowicz

2011

The Journal of the Acoustical Society of America

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This study investigated the relationship between audibility and predictions of speech recognition for children and adults with normal hearing. The Speech Intelligibility Index (SII) is used to quantify the audibility of speech signals and can be applied to transfer functions to predict speech recognition scores. Although the SII is used clinically with children, relatively few studies have evaluated SII predictions of children's speech recognition directly. Children have required more audibility than adults to reach maximum levels of speech understanding in previous studies. Furthermore, children may require greater bandwidth than adults for optimal speech understanding, which could influence frequency-importance functions used to calculate the SII. Speech recognition was measured for 116 children and 19 adults with normal hearing. Stimulus bandwidth and background noise level were varied systematically in order to evaluate speech recognition as predicted by the SII and derive frequency-importance functions for children and adults. Results suggested that children required greater audibility to reach the same level of speech understanding as adults. However, differences in performance between adults and children did not vary across frequency bands.

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“…The types of speech test material include various nonsense syllables tests (Fletcher and Steinberg, 1929;Pavlovic and Studebaker, 1984;Humes et al, 1986), CID-W22 or PB-words (Studebaker and Sherbecoe, 1991), NU-6 monosyllables , Diagnostic Rhyme Test (Duggirala et al, 1988), short passage of easy reading material (Studebaker et al, 1987), the monosyllables of the Speech in Noise Test (Bell et al, 1992) and average speech (Pavlovic, 1987). These importance functions are shown in Table 3.…”

Section: Principles and Calculation Methodsmentioning

confidence: 99%

Methods and Applications of the Audibility Index in Hearing Aid Selection and Fitting

Amlani

Punch

Ching

2002

Trends in Amplification

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During the first half of the 20th century, communications engineers at Bell Telephone Laboratories developed the articulation model for predicting speech intelligibility transmitted through different telecommunication devices under varying electroacoustic conditions. The profession of audiology adopted this model and its quantitative aspects, known as the Articulation Index and Speech Intelligibility Index, and applied these indices to the prediction of unaided and aided speech intelligibility in hearing-impaired listeners. Over time, the calculation methods of these indices-referred to collectively in this paper as the Audibility Index-have been continually refined and simplified for clinical use. This article provides (1) an overview of the basic principles and the calculation methods of the Audibility Index, the Speech Transmission Index and related indices, as well as the Speech Recognition Sensitivity Model, (2) a review of the literature on using the Audibility Index to predict speech intelligibility of hearing-impaired listeners, (3) a review of the literature on the applicability of the Audibility Index to the selection and fitting of hearing aids, and (4) a discussion of future scientific needs and clinical applications of the Audibility Index."Articulation measurement is a tedious and expensive business, and considerable attention has been devoted to the development of a computational device to replace, or at least to supplement,

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Frequency importance functions for a feature recognition test material

Cited by 32 publications

References 22 publications

Speech Evoked Potentials: From the Laboratory to the Clinic

Speech Evoked Potentials: From the Laboratory to the Clinic

Audibility-based predictions of speech recognition for children and adults with normal hearing

Methods and Applications of the Audibility Index in Hearing Aid Selection and Fitting

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