The two-pass information bottleneck (TPIB) based speaker diarization system operates independently on different conversational recordings. TPIB system does not consider previously learned speaker discriminative information while diarizing new conversations. Hence, the real time factor (RTF) of TPIB system is high owing to the training time required for the artificial neural network (ANN). This paper attempts to improve the RTF of the TPIB system using an incremental transfer learning approach where the parameters learned by the ANN from other conversations are updated using current conversation rather than learning parameters from scratch. This reduces the RTF significantly. The effectiveness of the proposed approach compared to the baseline IB and the TPIB systems is demonstrated on standard NIST and AMI conversational meeting datasets. With a minor degradation in performance, the proposed system shows a significant improvement of 33.07% and 24.45% in RTF with respect to TPIB system on the NIST RT-04Eval and AMI-1 datasets, respectively.
翻译:以双通道信息瓶颈(TPIB)为基础的扬声器二分化系统在不同对话录音上独立运作。TPIB系统在对新对话进行分解时,不考虑以前学到的言语歧视信息,因此,TPIB系统的实时因数(RTF)很高,因为人工神经网络(ANN)需要培训时间,本文件试图采用递增转移学习方法,改进TPIB系统的RTF, 即使用当前对话更新ANN从其他对话中学习的参数,而不是从零开始学习参数。这大大降低了RTF。拟议办法与基线IB和TPIB系统相比,在标准NIST和AMI对话会议数据集上显示的实效。由于性能稍有下降,拟议的系统显示在TRT-404Eval和AMI-1数据集的TPIB系统方面分别显著改进了33.07%和24.45%。