Acoustic-Based Automatic Addressee Detection for Technical Systems: A Review

Objective: Acoustic addressee detection is a challenge that arises in human group interactions, as well as in interactions with technical systems. The research domain is relatively new, and no structured review is available. Especially due to the recent growth of usage of voice assistants, this topic received increased attention. To allow a natural interaction on the same level as human interactions, many studies focused on the acoustic analyses of speech. The aim of this survey is to give an overview on the different studies and compare them in terms of utilized features, datasets, as well as classification architectures, which has so far been not conducted.

Methods: The survey followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. We included all studies which were analyzing acoustic and/or acoustic characteristics of speech utterances to automatically detect the addressee. For each study, we describe the used dataset, feature set, classification architecture, performance, and other relevant findings.

Results: 1,581 studies were screened, of which 23 studies met the inclusion criteria. The majority of studies utilized German or English speech corpora. Twenty-six percent of the studies were tested on in-house datasets, where only limited information is available. Nearly 40% of the studies employed hand-crafted feature sets, the other studies mostly rely on Interspeech ComParE 2013 feature set or Log-FilterBank Energy and Log Energy of Short-Time Fourier Transform features. 12 out of 23 studies used deep-learning approaches, the other 11 studies used classical machine learning methods. Nine out of 23 studies furthermore employed a classifier fusion.

Conclusion: Speech-based automatic addressee detection is a relatively new research domain. Especially by using vast amounts of material or sophisticated models, device-directed speech is distinguished from non-device-directed speech. Furthermore, a clear distinction between in-house datasets and pre-existing ones can be drawn and a clear trend toward pre-defined larger feature sets (with partly used feature selection methods) is apparent.