What is Speech Intelligibility?
The term Speech Intelligibility is generally used in two different ways. It can refer to how much speech is understood by a listener, or to the number of words correctly identified by a listener as a proportion or percentage of the total number of words. In the Clarity project, we are using the latter definition, i.e., the percentage of words in a sentence that a listener identified correctly. This percentage is the target for your prediction models.
Speech intelligibility captures how a listener's ability to participate in conversation is changed when the speech signal is degraded, e.g., by background noise and room reverberation, or is processed, e.g., by a hearing aid. Your prediction model will need to incorporate a model of the hearing abilities of each listener.
How is Speech Intelligibility measured with listeners?
In the Clarity project, a set of listeners listen to a sentence and then say what words they heard. In this project, speech intelligibility is measured as the number of words identified correctly as a percentage of the total number of words in a sentence.
You might consider looking at other metrics, such as Word Error Rate (WER), which picks up on, e.g., where listeners insert words not in the original sentence. You might do this if you think that an estimate of WER or other metrics would help your system to estimate speech intelligibility, as defined in the Clarity project.
How is Speech Intelligibility objectively measured by a computer?
When fitting a hearing aid, it would be beneficial for an audiologist to be able to use an objective measure of speech intelligibility to determine what signal processing algorithm(s) should be used to compensate for the listener's hearing impairment. Objective measures are also useful when measured speech intelligibility scores are unavailable, such as when developing a machine learning-based hearing aid algorithm or some other speech enhancement method. Another advantage of non-intrusive measures is that they do not require time-alignment of processed and reference signals.
Objective measures - or metrics - of speech intelligibility are used to allow a computer to estimate the likely performance of humans in listening tests. The main goal of entries to the prediction challenge is to produce one of these measures that performs well for listeners with hearing loss. There are two broad classes of speech intelligibility models:
- Intrusive metrics (also known as double-ended) are most common. This is where the intelligibility is estimated by comparing the degraded or processed speech signal with the original clean speech signal.
- Non-intrusive metrics (also known as single-ended or blind) are less well developed. This is where intelligibility is estimated from the degraded or processed speech signal alone.
In the Clarity project, both types of metrics are of interest. Intrusive metrics will be more accurate in many cases. However, there are hearing aid processes where the speech content is shifted in frequency, which will defeat most current intrusive speech intelligibility metrics. We also hypothesise that there might be issues with intrusive metrics and machine learning approaches in hearing aids that revoice the original speech.
What speech intelligibility models already exist and what are they used for?
There aren't many speech intelligibility models that consider hearing impairment, but one that does is HASPI by Kates and Arehart. In this seminar from the first Clarity workshop, James Kates discusses speech intelligibility models with a focus on the ones he has developed. He also discusses the speech quality metric HASQI. If you're interested in using HASPI or HASQI for the challenge, James Kates has kindly made the MATLAB code and user guide available for download.
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There are many types of hearing loss, but the focus of the Clarity project is the hearing loss that happens with ageing. This is a form of sensorineural hearing loss.
How does hearing loss affect the perception of audio signals, and how do modern hearing aids process sound to help with this?
In this seminar from the first Clarity workshop, Karolina Smeds from ORCA Europe and WS Audiology discusses the effects of hearing loss and the hearing aid processing strategies that are typically used to counter the sensory deficits.