WOZ acoustic data collection for interactive TV

By A. Brutti, L. Cristoforetti, Walter Kellermann, L. Marquardt, Maurizio Omologo. 2008

37 citations recorded by the source corpus.

This paper describes a multichannel acoustic data collection recorded under the European DICIT project, during Wizard of Oz (WOZ) experiments carried out at FAU and FBK-irst laboratories. The application of interest in DICIT is a distant-talking interface for control of interactive TV working in a typical living room, with many interfering devices. The objective of the experiments was to collect a database supporting efficient development and tuning of acoustic processing algorithms for signal enhancement. In DICIT, techniques for sound source localization, multichannel acoustic echo cancellation, blind source separation, speech activity detection, speaker identification and verification as well as beamforming are combined to achieve a maximum possible reduction of the user speech impairments typical of distant-talking interfaces. The collected database permitted to simulate at preliminary stage a realistic scenario and to tailor the involved algorithms to the observed user behaviors. In order to match the project requirements, the WOZ experiments were recorded in three languages: English, German and Italian. Besides the user inputs, the database also contains non-speech related acoustic events, room impulse response measurements and video data, the latter used to compute three-dimensional positions of each subject. Sessions were manually transcribed and segmented at word level, introducing also specific labels for acoustic events.

Subfields: Computational Mechanics, Evaluation & Benchmarks, Speech & Audio, Signal Processing

Citations in the corpus

• CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “The CHiME-1 [15] and CHiME-2 [16] challenges and corpora have contributed to popularizing research on this topic, together with the DICIT [17], Sweet-Home [18], and DIRHA [19] corpora.”
• A French Corpus for Distant-Microphone Speech Processing in Real Homes (2016, unknown): “The DICIT corpus [25] features a constrained scenario, with talkers sitting in front of a smart TV.”
• A Survey of Sound Source Localization with Deep Learning Methods (2021, unknown): “The recordings have been made using several types of microphone arrays, namely a planar array from [243], the em32 Eigenmike® spherical array, a hearing aid, and a set of microphones mounted on a robot head.”
• The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines (2015, unknown): “Another set of corpora, e.g. Aurora 2 and 4, HIWIRE, and DICIT, consider voice command scenarios in the presence of background noise, which is either simulated or scenarized [5–8].”
• The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines (2018, unknown): “The CHiME-1 [14] and CHiME-2 [15] challenges and corpora have contributed to popularizing research on this topic, together with the DICIT [16], Sweet-Home [17], and DIRHA [18] corpora.”
• A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “Aurora-321 car ∼20 4 1 static no AMI [294]22 meeting 100 16 3 static yes DICIT [295]23 TV order 6 16 1 moving no COSINE [296]24 discuss.”
• VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “The DICIT corpus (Brutti et al., 2008) features a constrained scenario, with talkers sitting in front of a smart TV.”