Speaker clustering using decision tree-based phone cluster models with multi-space probability distributions

Han Ping Shen, Jui Feng Yeh, Chung Hsien Wu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper presents an approach to speaker clustering using decision tree-based phone cluster models (DT-PCMs). In this approach, phone clustering is first applied to construct the universal phone cluster models to accommodate acoustic characteristics from different speakers. Since pitch feature is highly speaker-related and beneficial for speaker identification, the decision trees based on multi-space probability distributions (MSDs), useful to model both pitch and cepstral features for voiced and unvoiced speech simultaneously, are constructed. In speaker clustering based on DT-PCMs, contextual, phonetic, and prosodic features of each input speech segment is used to select the speaker-related MSDs from the MSD decision trees to construct the initial phone cluster models. The maximum-likelihood linear regression (MLLR) method is then employed to adapt the initial models to the speaker-adapted phone cluster models according to the input speech segment. Finally, the agglomerative clustering algorithm is applied on all speaker-adapted phone cluster models, each representing one input speech segment, for speaker clustering. In addition, an efficient estimation method for phone model merging is proposed for model parameter combination. Experimental results show that the MSD-based DT-PCMs outperform the conventional GMM- and HMM-based approaches for speaker clustering on the RT09 tasks.

Original languageEnglish
Article number5613154
Pages (from-to)1289-1300
Number of pages12
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number5
Publication statusPublished - 2011

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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