Research on individuality features in speech waves and automatic speaker recognition techniques
Speech Communication - Special issue: Speech research in Japan
Usefulness of the LPC-residue in text-independent speaker verification
Speech Communication
Speaker change detection in casual conversations using excitation source features
Speech Communication
A privacy-sensitive approach to modeling multi-person conversations
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The nonverbal structure of patient case discussions in multidisciplinary medical team meetings
ACM Transactions on Information Systems (TOIS)
Hi-index | 0.00 |
In this paper we investigate a set of privacy-sensitive audio features for speaker change detection (SCD) in multiparty conversations. These features are based on three different principles: characterizing the excitation source information using linear prediction residual, characterizing subband spectral information shown to contain speaker information, and characterizing the general shape of the spectrum. Experiments show that the performance of the privacy-sensitive features is comparable or better than that of the state-of-the-art full-band spectral-based features, namely, mel frequency cepstral coefficients, which suggests that socially acceptable ways of recording conversations in real-life is feasible.