Development of a Speech Quality Database Under Uncontrolled Conditions

Abstract

Objective audio quality assessment is preferred to avoid time-consuming and costly listening tests. The development of objective quality metrics depends on the availability of datasets appropriate to the application under study. Currently, a suitable human-annotated dataset for developing quality metrics in archive audio is missing. Given the online availability of archival recordings, we propose to develop a real-world audio quality dataset. We present a methodology used to curate a speech quality database using the archive recordings from the Apollo Space Program. The proposed procedure is based on two steps: a pilot listening test and an exploratory data analysis. The pilot listening test shows that we can extract audio clips through the control of speech-to-text performance metrics to prevent data repetition. Through unsupervised exploratory data analysis, we explore the characteristics of the degradations. We classify distinct degradations and we study spectral, intensity, tonality and overall quality properties of the data through clustering techniques. These results provide the necessary foundation to support the subsequent development of large-scale crowdsourced datasets for audio quality.