DNN and i-vector combined method for speaker recognition on multi-variability environments

Reyes-Díaz, Flavio J.; Hernández-Sierra, Gabriel; Lara, José Ramón Calvo de

doi:10.1007/s10772-021-09796-1

Cited by 4 publications

(1 citation statement)

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“…This space that models the variability of the speaker and the track simultaneously is the total factor space. The mapping of each speech segment in this space is called the identity vector (i-vector) 19 – 21 . However, the i-vector’s sensitivity to initial values can cause the model to fail to converge to an optimal solution and have limited ability to handle high-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

ECAPA-TDNN based online discussion activity-level evaluation

Kang,

He,

Song

et al. 2024

Sci Rep

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With the continuous development and application of online interactive activities and network transmission technology, online interactive behaviors such as online discussion meetings and online teaching have become indispensable in people’s studies and work. However, the effectiveness of working with online discussions and feedback from participants on their conference performance has been a major concern, and this is the issue examined in this post. Based on the above issues, this paper designs an online discussion activity-level evaluation system based on voiceprint recognition technology. The application system developed in this project is divided into two parts; the first part is to segment the online discussion audio into multiple independent audio segments by audio segmentation technology and train the voiceprint recognition model to predict the speaker’s identity in each separate audio component. In the second part, we propose a linear normalized online meeting activity-level calculation model based on the modified main indexes by traversing and counting each participant’s speaking frequency and total speaking time as the main indexes for activity-level evaluation. To make the evaluation results more objective, reasonable, and distinguishable, the activity score of each participant is calculated, and each participant’s activity-level in the discussion meeting is derived by combining the fuzzy membership function. To test the system’s performance, we designed an experiment with 25 participants in an online discussion meeting, with two assistants manually recording the discussion and a host moderating the meeting. The results of the experiment showed that the system’s evaluation results matched those recorded by the two assistants. The system can fulfill the task of distinguishing the level of activity of participants in online discussions.

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Section: Introductionmentioning

confidence: 99%