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2012· article· fr· W2313176503 on OpenAlexaboutno aff
Ryan W. McCreery

Bibliographic record

VenueThe Hearing Journal · 2012
Typearticle
Languagefr
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsHearing aidAudiologyConfusionPsychologyComputer scienceMedicineSpeech recognition

Abstract

fetched live from OpenAlex

Figure: iStockphoto.comClinicians are often faced with decisions about engaging frequency-lowering signal processing in their patients’ hearing aids. Frequency-lowering is designed to move high-frequency sounds to lower frequencies, where limited hearing and hearing aid bandwidth are less likely to reduce audibility. Increasing access to high-frequency sounds is particularly important for hearing-impaired children who may need high-frequency speech sound audibility to maximize speech understanding. (Arch Otolaryngol Head Neck Surg 2004;130[5]:556.)Figure: Ryan W. McCreery, PhDThe clinical decision-making process for frequency-lowering can be complicated. Four different approaches are currently available, and each manufacturer has a different philosophy about how to lower high-frequency sounds and whether it should be engaged at all times or only when selected by the patient. Widely varied outcomes across research studies, even with the same type of signal processing, add to the confusion. A commonly used and well researched type of frequency-lowering, nonlinear frequency compression (NFC) can help clinicians understand the factors that influence outcomes with this technology. NFC is the most commonly researched frequency-lowering. It has been investigated over the past few years in multiple studies with adults and children. Developing a cohesive picture of how and when to use NFC is complicated by mixed findings within participants in the same study and across studies even if clinicians have had the time to keep up with research in this area. Findings from one study showed nearly half of the adult participants had improved speech recognition with NFC compared with conventional amplification, while an equivalent number of listeners did not experience improvement with NFC. (Int J Audiol 2005;44[5]:281.) More recently speech recognition data for adults and children with hearing loss was reported. (Int J Audiol 2009;48[9]:632.) Another study reported mixed results in overall speech recognition with NFC, although younger participants and those with greater degrees of high-frequency hearing loss experienced more improved detection of high-frequency phonemes. (Int J Audiol 2005;44[5]:281.) These studies and others have expanded our understanding of how NFC affects speech understanding, but like all good research studies they raise additional questions. Will changes in perception with NFC occur immediately or does it take the listener time to reach maximum performance? How can NFC be optimized for individual patients? What types of speech recognition materials are sensitive to improved speech understanding with NFC? Evaluation of Nonlinear Frequency Compression for School-Age Children with Moderate to Moderately Severe Hearing Loss Wolfe J, John A, et al J Am Acad Audiol 2010;21[10]:618 Long-term Effects of Non-Linear Frequency Compression for Children with Moderate Hearing Loss Wolfe J, John A, et al Int J Audiol 2011;50[6]:396 Two papers from Wolfe et al attempted to address issues in a group of children aged 5 to 13 with mild to moderately severe hearing loss. Initially speech recognition in quiet was measured after a six-week trial with NFC using the University of Western Ontario (UWO) Plurals Test, vowel-consonant-vowel nonsense syllables, and the Bamford-Kowal-Bench Speech-in-Noise (BKB-SIN) test. (Am J Audiol 2012 Mar 12. [Epub]). Results suggested significantly improved plural discrimination, recognition of /s/ and /d/ phonemes, and detection of high-frequency sounds with NFC activated. No significant differences in sentence recognition in noise were reported. The improvements noted by researchers after six weeks in quiet were maintained or increased after six months of study, and improvements in sentence recognition in noise were reported. (Int J Audiol 2011;50[6]:396.) How can clinicians apply this knowledge to their pediatric patients with hearing aids? An important attribute of these two studies is audibility verification that occurred as part of the experimental procedure, similar to the procedures recommended for NFC. (Am J Audiol 2012 Mar 12. [Epub].) Verification must document that signal processing is improving high-frequency audibility if that is the goal of NFC and other frequency-lowering processing. The Frequency-Lowering Test in the Audioscan Verifit uses narrowband speech stimuli corresponding to high-frequency bands of speech energy to establish improvements in audibility with NFC. Clinicians can use this method with their own patients to determine if NFC results in improved audibility. Improvements in speech recognition should not be expected if high-frequency sounds are not more audible with NFC. Glista and Scollie developed a clinical verification method that can help clinicians to quantify audibility with NFC. (Audiology Online November 9, 2009.) Results from Wolfe et al also suggest that some NFC benefits may be observed after only a few weeks of experience while others may require listening experience before improvements are observed. Enhancement of speech recognition in quiet was observed after six weeks of exposure with NFC, but improvements in sentence recognition in noise were significant only after six months of use. Further research is needed to determine the time course and characteristics of this process, but these results suggest that speech recognition results at the initial fitting may not reflect longterm performance, particularly in children. The potential effects of maturation on the significant improvements in speech recognition in noise observed at six months should also be considered when monitoring speech recognition over time in children. Recognition differences across speech stimuli used by Wolfe et al highlight the importance of selecting appropriate stimuli for clinical speech recognition testing with NFC. Test stimuli that include contrasts that are likely to improve with NFC, such as the UWO Plurals Test, may be sensitive to improvements in audibility related to NFC. More complex stimuli, such as the BKB-SIN sentences, may provide a realistic estimate of how children function with linguistic context and in background noise. The child's ability to use linguistic cues in meaningful sentences, however, may overestimate improvements in speech recognition related to NFC. This contrast highlights the importance of using multiple types of speech stimuli of varying linguistic complexity to evaluate speech recognition in children with hearing aids. Clinicians must make decisions about using these strategies and assessing patient outcomes while NFC research continues to evolve. Establishing audibility through verification is an important first step for maximizing the potential benefit with frequency-lowering. Monitoring outcomes after fittings periodically, such as speech recognition and detection of high-frequency sounds, can give clinicians the data they need to support the use of frequency-lowering with individual patients. Listeners may require experience with the technology in everyday situations before significant changes in perception occur. Future research in this area promises to support clinical decision-making regarding these novel approaches to increasing audibility. FastLinks Visit HJ's Student Blog at http://bit.ly/HJStudentBlog. Check out HJ’s R&D Blog at http://bit.ly/RDBlog. Read past Journal Club columns in a special collection at http://bit.ly/JClubCollection. Click and Connect! Access the links in The Hearing Journal by reading this issue on our website or in our new iPad app, both available at thehearingjournal.com. Comments about this article? Write to HJ at [email protected]. Follow us on Twitter at twitter.com/hearingjournal and like us on Facebook at www.facebook.com/HearingJournal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.879
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0060.003
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.8790.796

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.326
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2012
Admission routes1
Has abstractyes

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