Inferring Hearing Loss from Learned Speech Kernels

Banerjee, Bonny; Kapourchali, Masoumeh Heidari; Najnin, Shamima; Mendel, Lisa Lucks; Lee, Sung-Min; Patro, Chhayakanta; Pousson, Monique

doi:10.1109/icmla.2016.0014

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Individuals with hearing impairment exhibit numerous error patterns including omission, substitution, and place of articulation errors, and detailed analysis of these errors can provide important information regarding these individuals' hearing deficiencies and subsequent production deficits (e.g., Najnin et al, 2016;Banerjee et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Corpus of deaf speech for acoustic and speech production research

Mendel

Lee

Pousson

et al. 2017

The Journal of the Acoustical Society of America

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A corpus of recordings of deaf speech is introduced. Adults who were pre- or post-lingually deafened as well as those with normal hearing read standardized speech passages totaling 11 h of .wav recordings. Preliminary acoustic analyses are included to provide a glimpse of the kinds of analyses that can be conducted with this corpus of recordings. Long term average speech spectra as well as spectral moment analyses provide considerable insight into differences observed in the speech of talkers judged to have low, medium, or high speech intelligibility.

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Section: Introductionmentioning

confidence: 99%

Corpus of deaf speech for acoustic and speech production research

Mendel

Lee

Pousson

et al. 2017

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

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Quantification of Automatic Speech Recognition System Performance on d/Deaf and Hard of Hearing Speech

Zhao,

Choi,

Koenecke

et al. 2024

The Laryngoscope

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ObjectiveTo evaluate the performance of commercial automatic speech recognition (ASR) systems on d/Deaf and hard‐of‐hearing (d/Dhh) speech.MethodsA corpus containing 850 audio files of d/Dhh and normal hearing (NH) speech from the University of Memphis Speech Perception Assessment Laboratory was tested on four speech‐to‐text application program interfaces (APIs): Amazon Web Services, Microsoft Azure, Google Chirp, and OpenAI Whisper. We quantified the Word Error Rate (WER) of API transcriptions for 24 d/Dhh and nine NH participants and performed subgroup analysis by speech intelligibility classification (SIC), hearing loss (HL) onset, and primary communication mode.ResultsMean WER averaged across APIs was 10 times higher for the d/Dhh group (52.6%) than the NH group (5.0%). APIs performed significantly worse for “low” and “medium” SIC (85.9% and 46.6% WER, respectively) as compared to “high” SIC group (9.5% WER, comparable to NH group). APIs performed significantly worse for speakers with prelingual HL relative to postlingual HL (80.5% and 37.1% WER, respectively). APIs performed significantly worse for speakers primarily communicating with sign language (70.2% WER) relative to speakers with both oral and sign language communication (51.5%) or oral communication only (19.7%).ConclusionCommercial ASR systems underperform for d/Dhh individuals, especially those with “low” and “medium” SIC, prelingual onset of HL, and sign language as primary communication mode. This contrasts with Big Tech companies' promises of accessibility, indicating the need for ASR systems ethically trained on heterogeneous d/Dhh speech data.Level of Evidence3 Laryngoscope, 2024

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Inferring Hearing Loss from Learned Speech Kernels

Cited by 2 publications

References 27 publications

Corpus of deaf speech for acoustic and speech production research

Corpus of deaf speech for acoustic and speech production research

Quantification of Automatic Speech Recognition System Performance on d/Deaf and Hard of Hearing Speech

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