Conversational Quality Monitor Sample Clauses

Conversational Quality Monitor. This module provides four main functions as follows: ● Automatic measurement of conversational quality based on environmental noise on the robot side (Function 1). This feature was already included in the version 1.0 of the telepresence system. Particularly, it uses the adaptive Wiener filter [12]. ● Automatic measurement of conversational quality based on listener-oriented speech on the pilot side (Function 2). This feature was already included in the version 2.0 but extend to more various states: co-presence, arousal, and valence of emotion. ● Voice feature extraction (Function 3). This feature has been newly included in the version of 3.0 to provide low-level voice feature (e.g. duration and energy of speech). ● Engagement level in multi-party conversation on the interaction target side (Function 4). This feature has been newly included in the version of 3.0 to provide individual engagement rank in a group. Function 2 has been extended to detect more various affective status: co-presence, arousal, and valence of emotion. Moreover, it does not have any dependency on specific statistical models. Rather, it can use any models as long as they are trained using WEKA implementations. The pre-trained models are Support Vector Machines with Sequential Minimal Optimisation built using AIBO, EMODB, VAM, SEMAINE, and ENTERFACE corpora [13], which are widely used in speech emotion recognition tasks. Function 3 extracts voice features which are mainly used for social signal processing. Using adaptive Gaussian Mixture Models, it tests whether input frames are noise or speech. It provides with different detection modes: normal, low-bit, aggressive, and very aggressive, which could be chosen for different situations. Function 4 detects who is the most engaged on in a group conversation. It uses a pre-trained SVM RANK model and vocalic turn-taking features (e.g. overlaps and interruptions) to order subjects in terms of engagement levels. At this moment, this function relies on individual microphone since we do not have beam-forming and source separation modules. Hence, each subject is required to wear own wireless microphone for its demonstration. Inputs: (Processed data from the onboard microphone array) Outputs: Provides: Required by: Connection Type: Excessive Background Noise Events (Func. 1) Body-pose Controller ACTIVEMQ | TCPROS Affective Status of User (Func. 2) Body-pose Controller ACTIVEMQ | TCPROS Voice Feature (Func. 3) Body-pose Controller, Behavior Manager ...
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