Long-term Conversation Analysis: Exploring Utility and Privacy
The analysis of conversations recorded in everyday life requires privacy protection. In this contribution, we explore a privacy-preserving feature extraction method based on input feature dimension reduction, spectral smoothing and the low-cost speaker anonymization technique based on McAdams coefficient. We assess the utility of the feature extraction methods with a voice activity detection and a speaker diarization system, while privacy protection is determined with a speech recognition and a speaker verification model. We show that the combination of McAdams coefficient and spectral smoothing maintains the utility while improving privacy.
Code (1)
Tasks
Action DetectionActivity DetectionDimensionality ReductionPrivacy PreservingSpeaker anonymizationspeaker-diarizationSpeaker DiarizationSpeaker Verificationspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support
The human-computer interaction (HCI) research community has a longstanding interest in exploring the mismatch between users' actual experiences and expectation toward new technologies, for instance, large language models…
Recommendation SystemsConv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation
Most recommendation benchmarks evaluate how well a model imitates user behavior. In financial advisory, however, observed actions can be noisy or short-sighted under market volatility and may conflict with a user's long-…
Should RAG Chatbots Forget Unimportant Conversations? Exploring Importance and Forgetting with Psychological Insights
While Retrieval-Augmented Generation (RAG) has shown promise in enhancing long-term conversations, the increasing memory load as conversations progress degrades retrieval accuracy. Drawing on psychological insights, we p…
RAGRetrievalRetrieval-augmented GenerationLong-Term Conversation Analysis: Privacy-Utility Trade-off under Noise and Reverberation
Recordings in everyday life require privacy preservation of the speech content and speaker identity. This contribution explores the influence of noise and reverberation on the trade-off between privacy and utility for lo…
Action DetectionActivity DetectionEdge-computingPrivacy Preserving+4ConVIScope: Visual Analytics for Exploring Patient Conversations
The proliferation of text messaging for mobile health is generating a large amount of patient-doctor conversations that can be extremely valuable to health care professionals. We present ConVIScope, a visual text analyti…