Incremental Transfer Learning In Two-pass Information Bottleneck Based Speaker Diarization System For Meetings

Abstract

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 o

Stats

• citations6
• S2 citations—
• github stars0
• HF likes0
• heat score6.34
• arxiv keydawalatabad2019incremental

Related papers

• Novel Architectures For Unsupervised Information Bottleneck Based Speaker Diarization Of Meetings (2020)8.09
• Once More Diarization: Improving Meeting Transcription Systems Through Segment-level Speaker Reassignment (2024)5.24
• Integration Of Speech Separation, Diarization, And Recognition For Multi-speaker Meetings: System Description, Comparison, And Analysis (2020)13.23
• Tandem Multitask Training Of Speaker Diarisation And Speech Recognition For Meeting Transcription (2022)7.81
• Toeplitz Inverse Covariance Based Robust Speaker Clustering For Naturalistic Audio Streams (2019)0.00
• Exploring Speaker-related Information In Spoken Language Understanding For Better Speaker Diarization (2023)0.00
• Speaker Diarization Using Two-pass Leave-one-out Gaussian PLDA Clustering Of DNN Embeddings (2021)2.26
• Enhancements For Audio-only Diarization Systems (2019)0.00