BSP engineering pipelines can take raw signal data and transform them into actionable insights on behavior and emotion. The engineering components are quite specific to the actual application, but there are general attributes of any such project. There are three primary building blocks of BSP:
An example engineering component of an end-to-end pipeline is that of speaker diarization, the process of determining who spoke when, for how long. Following up with speech-to-text can also deliver what they spoke about (this process is illustrated below). This component of the pipeline informs who is doing what, but it still not sufficient for measuring the higher level behaviors such as emotion and attributes of interaction success.
Fig.4 Converting a raw speech signal into a spectrogram (energies in various frequencies, over time), detecting voice, determining who is speaking, and transcribing the speech; then using the how and the what to track emotions, behaviors, and KPIs.
1 – Behavioral & Emotional Analytics | 2 – BSP in Healthcare | 3 – BSP for Media & Movie Analytics | 4 – BSP for Home Assistants & Robotics | 5 – BSP for Contact Centers | 6 – BSP for Organizational Behavior | 7 – Behavioral Signal Processing Pipeline: How it all works | 8 – Our Platform: callER & textER | 9 -The Challenges of Quantifying Behavior from Signals