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Using Emotion Recognition and Dialog Analysis to Detect Trouble in Communication in Spoken Dialog Systems. Nathan Imse Kelly Peterson. Goal -- Detecting Trouble. Problem in communication grow quickly when the system does not recognize that an error occurred
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Using Emotion Recognition and Dialog Analysis to Detect Trouble in Communication in Spoken Dialog Systems Nathan Imse Kelly Peterson
Goal -- Detecting Trouble • Problem in communication grow quickly when the system does not recognize that an error occurred • Spikes in emotions like anger and frustration are highly correlated with problems in such systems. • In realistic data, 'pure' emotions may not be present, so we are looking at 'troubles in communication' (Batliner et al, 2003) • This is a binary distinction • Dialog patterns that break from the norm are also highly indicative of trouble; being able to detect those breaks would help in keeping dialogs from getting out of hand.
Proposals • Our proposals : • Module additions and enhancements • Architecture enhancements • Data for our analysis • DARPA Communicator 2000/2001 data (dialog acts) • Ang et al. (2002) annotations of emotion (anger, frustration, neutral)
Acoustic Features • Features pertaining to the audio signal • pitch • intensity • duration • voice quality • Huge feature space • Feature selection/pruning is critical
Linguistic Features (aka non-acoustic) • Pretty much any feature that isn't directly extracted from the audio signal • Usually based on text • ASR - easy/fast, but error-prone • Transcription - precise, but slow/expensive • cannot be done in real time • Shallow • ngrams, POS tagging, etc... • Deep • dialog acts, specialized grammars, etc...
Module Proposals • Emotion Grammar => build numbers from statistical training • normalize for depth/complexity of sentences? • Dialog Acts => build a statistical model of dialog act sequences; use perplexity to detect problems • how far should the horizon be? • System Repetition => don't let the system repeat itself too many times • simple flag => set and forget; minimal computing power • classifier => captures exceptions; flexible
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