Can we Automatically Transform Speech Recorded on Common Consumer Devices in Real-World Environments into Professional Production Quality Speech?—A Dataset, Insights, and Challenges

Abstract

The goal of speech enhancement is typically to recover clean speech from noisy, reverberant, and often bandlimited speech in order to yield improved intelligibility, clarity, or automatic speech recognition performance. However, the acoustic goal for a great deal of speech content such as voice overs, podcasts, demo videos, lecture videos, and audio stories is often not merely clean speech, but speech that is aesthetically pleasing. This is achieved in professional recording studios by having a skilled sound engineer record clean speech in an acoustically treated room and then edit and process it with audio effects (which we refer to as production). A growing amount of speech content is being recorded on common consumer devices such as tablets, smartphones, and laptops. Moreover, it is typically recorded in common but non-acoustically treated environments such as homes and offices. We argue that the goal of enhancing such recordings should not only be to make it sound cleaner as would be done using traditional speech enhancement techniques, but to make it sound like it was recorded and produced in a professional recording studio. In this paper, we show why this can be beneficial, describe a new data set (a great deal of which was recorded in a professional recording studio) that we prepared to help in developing algorithms for this purpose, and discuss some insights and challenges associated with this problem.