The Chinese Zodiac-in-Noise Test: An Internet-Based Speech-in-Noise Test for Large-Scale Hearing Screening.

OBJECTIVES: Motivated by the growing need for hearing screening in China, the present study has two objectives. First, to develop and validate a new test, called the Chinese Zodiac-in-noise (ZIN) test, for large-scale hearing screening in China. Second, to conduct a large-scale remote hearing screening in China, using the ZIN test developed. DESIGN: The ZIN test was developed following a similar procedure as the digits-in-noise test but emphasizes the importance of consonant recognition by employing the 12 zodiac animals in traditional Chinese culture as speech materials. It measures the speech reception threshold (SRT) using triplets of Chinese zodiac animals in speech-shaped noise with an adaptive procedure. RESULTS: Normative data of the test were obtained in a group of 140 normal-hearing listeners, and the performance of the test was validated by comparisons with pure-tone audiometry in 116 listeners with various hearing abilities. The ZIN test has a reference SRT of -11.0 ± 1.6 dB in normal-hearing listeners with a test-retest variability of 1.7 dB and can be completed in 3 minutes. The ZIN SRT is highly correlated with the better-ear pure-tone threshold ( r = 0.82). With a cutoff value of -7.7 dB, the ZIN test has a sensitivity of 0.85 and a specificity of 0.94 for detecting a hearing loss of 25 dB HL or more at the better ear.A large-scale remote hearing screening involving 30,552 participants was performed using the ZIN test. The large-scale study found a hearing loss proportion of 21.0% across the study sample, with a high proportion of 57.1% in the elderly study sample aged over 60 years. Age and gender were also observed to have associations with hearing loss, with older individuals and males being more likely to have hearing loss. CONCLUSIONS: The Chinese ZIN test is a valid and efficient solution for large-scale hearing screening in China. Its remote applications may improve access to hearing screening and enhance public awareness of hearing health.

Deep Learning-Based Speech Enhancement With a Loss Trading Off the Speech Distortion and the Noise Residue for Cochlear Implants.

The cochlea plays a key role in the transmission from acoustic vibration to neural stimulation upon which the brain perceives the sound. A cochlear implant (CI) is an auditory prosthesis to replace the damaged cochlear hair cells to achieve acoustic-to-neural conversion. However, the CI is a very coarse bionic imitation of the normal cochlea. The highly resolved time-frequency-intensity information transmitted by the normal cochlea, which is vital to high-quality auditory perception such as speech perception in challenging environments, cannot be guaranteed by CIs. Although CI recipients with state-of-the-art commercial CI devices achieve good speech perception in quiet backgrounds, they usually suffer from poor speech perception in noisy environments. Therefore, noise suppression or speech enhancement (SE) is one of the most important technologies for CI. In this study, we introduce recent progress in deep learning (DL), mostly neural networks (NN)-based SE front ends to CI, and discuss how the hearing properties of the CI recipients could be utilized to optimize the DL-based SE. In particular, different loss functions are introduced to supervise the NN training, and a set of objective and subjective experiments is presented. Results verify that the CI recipients are more sensitive to the residual noise than the SE-induced speech distortion, which has been common knowledge in CI research. Furthermore, speech reception threshold (SRT) in noise tests demonstrates that the intelligibility of the denoised speech can be significantly improved when the NN is trained with a loss function bias to more noise suppression than that with equal attention on noise residue and speech distortion.