University Of Electro-Communications E-Bulletin: Speech Signal Processing Based On Shallow Neural Networks
University of Electro-Communications publishes the December 2021 issue of UEC e-Bulletin
December 2021 issue of UEC e-Bulletin
The December 2021 issue of the UEC e-Bulletin includes a video profile of UEC Associate Professor Toru Nakashika describing his recent research on “Speech Signal Processing Based on Shallow Neural Networks”.
The Research Highlights are ‘Frequency analysis helps to understand sleep disorder’, Keiki Takadama; and ‘Educational measurement/Modelling performance assessment’, Masaki Uto.
The Topics column is an interview with Eriko Watanabe, Associate Professor, Department of Engineering Science, offering insights into ‘Fascination with digital holograms and their applications for imaging through semi-opaque materials’.
Sleep apnea syndrome (SAS) is a sleep disorder characterized by the occurrence of pauses in breathing (apnea) during sleep. Such pauses can typically last for more than 10 seconds and are often followed by loud snoring. The brain interprets each breathing pause as danger — because of the decrease in oxygen supply — and sleep becomes shallow. As a result, a person suffering from SAS builds up a sleep debt, which may in turn lead to mental health issues like depression or dementia. In order to avoid medical complications, early detection of SAS is crucial. So-called non-contact detection methods are based on monitoring chest motion, e.g. by means of a sensor attached to the mattress sensor the person is sleeping on; from the recorded bio-vibration data, breathing frequencies and amplitudes can be derived. This type of method is not always effective. For example, when a person’s breathing is ‘forced’ (breathing accompanied by thoracic and abdomen movement, and in fact also a symptom of SAS), sleep apnea is difficult to detect.
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The researchers analysed bio-vibration data recorded from 9 SAS patients and 9 healthy individuals, obtained by means of a mattress sensor. Rather than looking only at respiration (between 0.1 Hz and 0.2 Hz) and heartbeat (between 0.6 Hz and 1.5 Hz) frequencies, they considered frequencies up to 8 Hz, and looked at the distribution — the spectrum — of frequencies. When comparing frequency spectra, Nakari and Takadama noticed a slight increase in frequency density around 3 Hz for the SAS patients. On a logarithmic plot of the frequency spectrum, this increase manifests itself as a convex shape. Based on this observation, the researchers defined a quantity called the degree of convexity of the logarithmic spectrum (DCLS).
Remarkably, the average DCLS value for the SAS patients (≈ 99 ± 10) is completely separate from the average value for the healthy subjects (≈ 48 ± 7). Therefore, the DCLS value has the potential to be used as an indicator for SAS — obtained just by sleeping on a mattress sensor.
Further analysis showed that the increased frequency density around 3 Hz corresponds to accumulated density in the so-called WAKE stage (the first of six levels used for characterizing ‘sleep deepness’). Therefore, it is likely that the WAKE stage is different for SAS patients and people not suffering from sleep apnea. Even more, the researchers argue that SAS subjects generate 3 Hz waves during WAKE phases, and believe that this may actually be a hitherto unknown symptom of SAS, apart from the apnea itself. However, as Nakari and Takadama point out, future work “should clarify the phenomenon around 3 Hz”.
Educational measurement: Modelling performance assessment
Performance assessment of a practical task carried out by an examinee is typically done by human raters awarding scores for different parts of the task. Often, a so-called scoring rubric is used for this purpose, listing the various parts and descriptions of the performance scores associated with them. There are some inherent shortcomings to this procedure, however, including the characteristics of the rubric’s evaluation items and the raters’ behaviour — one rater may score differently than another. Now, Masaki Uto from the University of Electro-Communications has developed a new model that takes into account the specifics of a rubric’s evaluation items and the raters.
The approach followed by Uto relies on models developed in a theoretical framework known as item response theory. It is based on a formula giving the probability Pijkr that examinee j gets score k for evaluation item i by rater r. The formula typically contains parameters such as the difficulty (βi) for the evalution item, the latent ability of the examinee (θj) and the severity of the rater (βr). The idea is then that, by fitting the formula to an existing dataset with known score outcomes, good values of the parameters (like βi, θj and βr) can be obtained. Yet, this description is almost always too simplistic to result in good results, however.
One improvement lies in incorporating the notion of ability dimensions — an abstract representation of an examinee having different ability ‘spheres’. Uto’s model combines ability dimensions with rater characteristics, which signifies a step forward in item response theory modelling.
Apart from providing a more realistic description of performance assessment with a rubric and raters, the model can also help to check the quality of the rubric’s evaluation items, as well as providing insights into what exactly each ability dimension measures.
Uto tested the probability formula by first simulating a large number of data sets, with randomly generated parameters. Then, the data sets were fitted to the formula, resulting in estimated parameters. Good agreement between the true and the fitted parameters was obtained, showing that the model works well. Moreover, specific simulations showed that the inclusion of rater characteristics led to improved examinee ability accuracy.
The model was also tested in actual data experiments, with 134 Japanese university students performing an essay-writing task requiring no preliminary knowledge. One conclusion was that, for this case, a two-dimensionality assumption worked better than a one-dimensional ability. A further finding was that the inclusion of rater characteristics indeed improved model fitting.
Uto plans to further test the model’s effectiveness using various and more massive datasets, and to, quoting the researcher, “extend the proposed model to four-way data consisting of examinees × raters × evaluation items × performance tasks because practical tests often include several tasks.”
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