Speaker Diarization Using Stereo Audio Channels: Preliminary Study on Utterance Clustering

Speaker diarization is one of the actively researched topics in audio signal processing and machine learning. Utterance clustering is a critical part of a speaker diarization task. In this study, we aim to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. We generated processed audio signals by combining left- and right-channel audio signals in a few different ways and then extracted embedded features (also called d-vectors) from those processed audio signals. We applied the Gaussian mixture model (GMM) for supervised utterance clustering. In the training phase, we used a parameter sharing GMM to train the model for each speaker. In the testing phase, we selected the speaker with the maximum likelihood as the detected speaker. Results of experiments with real audio recordings of multi-person discussion sessions showed that our proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono audio signals.

Capturing the Production of the Innovative Ideas: An Online Social Network Experiment and "Idea Geography" Visualization

Collective design and innovation are crucial in organizations. To investigate how the collective design and innovation processes would be affected by the diversity of knowledge and background of collective individual members, we conducted three collaborative design task experiments which involved nearly 300 participants who worked together anonymously in a social network structure using a custom-made computer-mediated collaboration platform. We compared the idea generation activity among three different background distribution conditions (clustered, random, and dispersed) with the help of the "doc2vec" text representation machine learning algorithm. We also developed a new method called "Idea Geography" to visualize the idea utility terrain on a 2D problem domain. The results showed that groups with random background allocation tended to produce the best design idea with highest utility values. It was also suggested that the diversity of participants' backgrounds distribution on the network might interact with each other to affect the diversity of ideas generated. The proposed idea geography successfully visualized that the collective design processes did find the high utility area through exploration and exploitation in collaborative work.

Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theory approach to collective decision making, agent-based simulations were conducted to investigate how collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing non-trivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed that collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multi-level decision making are discussed.