Domestic environments are particularly challenging for distant speech recognition: reverberation, background noise and interfering sources, as well as the propagation of acoustic events across adjacent rooms, critically degrade the performance of standard speech processing algorithms. In this application scenario, a crucial task is the detection and localization of speech events generated by users within the various rooms. A specific challenge of multi-room environments is the inter-room interference that negatively affects speech activity detectors. In this paper, we present and compare different solutions for the multi-room speech activity detection task. The combination of a model-based room-independent speech activity detection module with a room-dependent inside/outside classification stage, based on specific features, provides satisfactory performance. The proposed methods are evaluated on a multi-room, multi-channel corpus, where spoken commands and other typical acoustic events occur in different rooms.

No abstract

No abstract

Abstract-In this paper, we examine three problems that rise in the modern, challenging area of far-field speech processing. The developed methods for each problem, namely (a) multichannel speech enhancement, (b) voice activity detection, and (c) speech recognition, are potentially applicable to a distant speech recognition system for voice-enabled smart home environments. The obtained results on real and simulated data, regarding the smart home speech applications, are quite promising due to the accomplished improvements made in the employed signal processing methods.

No abstract