Quality Control in Remote Speech Data Collection

Abstract

There is the need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers which deserve further investigation. In this paper, a simple and effective approach for identification of outliers in a speech database is proposed. Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold. The DetMCD is a computationally efficient algorithm which provides a highly robust estimate of the mean and covariance in multivariate data even when 50% of the data are outliers. Experimental results using 8 different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4% accuracy, resulting in significantly decreasing the effort required for manually controlling the quality of the database.

Using audio-mixing software to facilitate remote data collection of conversational interactions

“…Regarding academia, it is reported that 78% of UK Higher Education staff wish to continue with hybrid-working (Taylor et al, 2021), higher education managers' attitudes regarding staff homeworking are also significantly more positive now than prepandemic (Forbes et al, 2020). As a result, there is the need to consider practical constraints for remote data collection, as there are several aspects which need to be considered as we rely more on online technology platforms for data collection and there is a growing body of research in this field that have considered the various challenges involved (e.g., Zhang et al, 2021;Poorjam et al, 2019;Gregory et al, 2022). The following section aims to consider some key practical areas which might in turn serve as a framework to evaluate the practical considerations for optimal data collection.…”

Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

“…Quality control of the voice recordings is typically performed manually by human experts which is a very costly and time consuming task, and is often infeasible in online applications. In [17], the problem of quality control in remote speech data collection has been approached by identifying the potential outliers which are inconsistent, in terms of the quality and the context, with the majority of speech samples in a data set. Even though very effective in finding outliers, it is not capable of detecting the type of degradation nor identifying short-term protocol violations in recordings.…”