Speaker Identification for Business-Card-Type Sensors

Human collaboration has a great impact on the performance of multi-person activities. The analysis of speaker information and speech timing can be used to extract human collaboration data in detail. Some studies have extracted human collaboration data by identifying a speaker with business-card-type sensors. However, it is difficult to realize speaker identification for business-card-type sensors at low cost and high accuracy because of spikes in the measured sound pressure data, ambient noise in the non-speaker sensor, and synchronization errors across each sensor. This study proposes a novel sound pressure sensor and speaker identification algorithm to realize speaker identification for business-card-type sensors. The sensor extracts the user&#x0027;s speech at low cost and high accuracy by employing a peak hold circuit and time synchronization module for spike mitigation and precise time synchronization. The algorithm identifies a speaker with high accuracy by removing ambient noise. The evaluations show that the algorithm accurately identifies a speaker in a multi-person activity considering varying numbers of users, environmental noises, and reverberation conditions as well as long or short utterances. In addition, the peak hold circuit enables accurate extraction of speech and the synchronization error between the sensors is always within <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>30 <inline-formula><tex-math notation="LaTeX">$\boldsymbol\mu$</tex-math></inline-formula>s, that is, negligible error.

Abstract licence: CC BY-NC-ND

56 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.