Automatic speech recognition predicts speech intelligibility and comprehension for listeners with simulated age-related hearing loss

Purpose: To assess speech processing for listeners with simulated age-related hearing loss (ARHL) and to investigate whether the observed performance can be replicated using an Automatic Speech Recognition (ASR) system. The long-term goal of this research is to develop a system that will assist audiologists/hearing-aid dispensers in the fine-tuning of hearing aids. Method: Sixty young normal-hearing participants listened to speech materials mimicking the perceptual consequences of ARHL at different levels of severity. Two intelligibility tests (repetition of words and sentences) and one comprehension test (responding to oral commands by moving virtual objects) were administered. Several language models were developed and used by the ASR system in order to fit human performances. Results: Strong significant positive correlations were observed between human and ASR scores, with coefficients up to .99. However, the spectral smearing used to simulate losses in frequency selectivity caused larger declines in ASR performance than in human performance. Conclusion: Both intelligibility and comprehension scores for listeners with simulated ARHL are highly correlated with the performances of an ASR-based system. In the future, it needs to be determined if the ASR system is similarly successful in predicting speech processing in noise and by older people with ARHL

Communication Biophysics

Contains reports on six research projects.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 5 RO1 NS18682)National Institutes of Health (Grant 5 RO1 NS20322)National Institutes of Health (Grant 5 R01 NS20269)National Institutes of Health (Grant 5 T32NS 07047)Symbion, Inc.National Science Foundation (Grant BNS 83-19874)National Science Foundation (Grant BNS 83-19887)National Institutes of Health (Grant 6 RO1 NS 12846)National Institutes of Health (Grant 1 RO1 NS 21322

Improving the Speech Intelligibility By Cochlear Implant Users

In this thesis, we focus on improving the intelligibility of speech for cochlear implants (CI) users. As an auditory prosthetic device, CI can restore hearing sensations for most patients with profound hearing loss in both ears in a quiet background. However, CI users still have serious problems in understanding speech in noisy and reverberant environments. Also, bandwidth limitation, missing temporal fine structures, and reduced spectral resolution due to a limited number of electrodes are other factors that raise the difficulty of hearing in noisy conditions for CI users, regardless of the type of noise. To mitigate these difficulties for CI listener, we investigate several contributing factors such as the effects of low harmonics on tone identification in natural and vocoded speech, the contribution of matched envelope dynamic range to the binaural benefits and contribution of low-frequency harmonics to tone identification in quiet and six-talker babble background. These results revealed several promising methods for improving speech intelligibility for CI patients. In addition, we investigate the benefits of voice conversion in improving speech intelligibility for CI users, which was motivated by an earlier study showing that familiarity with a talker’s voice can improve understanding of the conversation. Research has shown that when adults are familiar with someone’s voice, they can more accurately – and even more quickly – process and understand what the person is saying. This theory identified as the “familiar talker advantage” was our motivation to examine its effect on CI patients using voice conversion technique. In the present research, we propose a new method based on multi-channel voice conversion to improve the intelligibility of transformed speeches for CI patients