Speech Science and Technology in Hearing Aids



Hearing loss is a prevalent issue that affects a significant portion of the population. The statistics reveal that one in six people experience hearing loss, and this number is expected to rise significantly by 2030. The aging population is particularly vulnerable to hearing loss, with one in four people over 65 years old experiencing it. However, the younger generation is also at risk due to exposure to excessive noise, which is becoming increasingly common in our daily lives. With the advent of new technologies and the prevalence of portable music players, young people are exposed to loud music for extended periods, which can cause permanent damage to their hearing.

The consequences of hearing loss are significant, as it is linked to several adverse health outcomes. Hearing loss can lead to social isolation, loneliness, and depression. It can also affect cognitive function and lead to dementia, particularly in older adults. The impact of hearing loss on quality of life is profound, with individuals experiencing communication difficulties, reduced job opportunities, and diminished overall well-being. The economic impact of hearing loss is also significant, with estimates suggesting that it costs billions of dollars annually in lost productivity and healthcare costs.

To address the issue of hearing loss, hearing aids are commonly used to assist individuals with hearing loss. However, hearing aids have limitations, particularly in noisy environments. Many individuals with hearing loss find it challenging to communicate in noisy situations, such as restaurants, public transport, or social gatherings. The inability to hear adequately in these situations can lead to frustration and further isolation. Therefore, improving the performance of hearing aids in noisy environments is crucial to enable individuals with hearing loss to participate fully in society and improve their quality of life.


Related Publications

  1. EUSIPCO2023
    Auditory Model Optimization with Wavegram-CNN and Acoustic Parameter Models for Nonintrusive Speech Intelligibility Prediction in Hearing Aids.
    Candy Olivia Mawalim, Benita Angela Titalim, Shogo Okada, and Masashi Unoki.

    The 31st European Signal Processing Conference (EUSIPCO 2023), Helsinki, Finland, (To Appear).

    DOI PDF
  2. SST
    OBISHI: Objective Binaural Intelligibility Score for the Hearing Impaired.
    Candy Olivia Mawalim, Benita Angela Titalim, Masashi Unoki, and Shogo Okada.

    SST2022, The 18th Australasian International Conference on Speech Science and Technology, Canberra, Australia, 13--16 December 2022.

    PDF
    Speech intelligibility prediction for both normal hearing and hearing impairment is very important for hearing aid development. The Clarity Prediction Challenge 2022 (CPC1) was initiated to evaluate the speech intelligibility of speech signals produced by hearing aid systems. Modified binaural short-time objective intelligibility (MBSTOI) and hearing aid speech prediction index (HASPI) were introduced in the CPC1 to understand the basis of speech intelligibility prediction. This paper proposes a method to predict speech intelligibility scores, namely OBISHI. OBISHI is an intrusive (non-blind) objective measurement that receives binaural speech input and considers the hearing-impaired characteristics. In addition, a pre-trained automatic speech recognition (ASR) system was also utilized to infer the difficulty of utterances regardless of the hearing loss condition. We also integrated the hearing loss model by the Cambridge auditory group and the Gammatone Filterbank-based prediction model. The total evaluation was conducted by comparing the predicted intelligibility score of the baseline MBSTOI and HASPI with the actual correctness of listening tests. In general, the results showed that the proposed method, OBISHI, outperformed the baseline MBSTOI and HASPI (improved approximately 10% classification accuracy in terms of F1 score).
  3. APSIPA
    Speech Intelligibility Prediction for Hearing Aids Using an Auditory Model and Acoustic Parameters.
    Benita Angela Titalim*, Candy Olivia Mawalim*, Shogo Okada, and Masashi Unoki.

    2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Chiang Mai, Thailand, 7--10 November 2022.

    PDF
    Objective speech intelligibility (SI) metrics for hearing-impaired people play an important role in hearing aid development. The work on improving SI prediction also became the basis of the first Clarity Prediction Challenge (CPC1). This study investigates a physiological auditory model called EarModel and acoustic parameters for SI prediction. EarModel is utilized because it provides advantages in estimating human hearing, both normal and impaired. The hearing-impaired condition is simulated in EarModel based on audiograms; thus, the SI perceived by hearing-impaired people is more accurately predicted. Moreover, the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) and WavLM, as additional acoustic parameters for estimating the difficulty levels of given utterances, are included to achieve improved prediction accuracy. The proposed method is evaluated on the CPC1 database. The results show that the proposed method improves the SI prediction effects of the baseline and hearing aid speech prediction index (HASPI). Additionally, an ablation test shows that incorporating the eGeMAPS and WavLM can significantly contribute to the prediction model by increasing the Pearson correlation coefficient by more than 15% and decreasing the root-mean-square error (RMSE) by more than 10.00 in both closed-set and open-set tracks.