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This page is providing information about double articulation analyzer (DAA), which can estimate latent double articulation structure embedded on a multidimensional time series data, e.g., speech signals, human motion data, and driving behavior data. For the purpose and backgrounds of the DAA, please see "What's DA?"

<Citations>

    1. Tadahiro Taniguchi, Shogo Nagasaka, Ryo Nakashima, Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals, IEEE Transactions on Cognitive and Developmental Systems, Vol.8 (3), pp. 171-185 .(2016) DOI: 10.1109/TCDS.2016.2550591 (Open Access) [LINK]

        • The original paper of the nonparametric Bayesian double articulation analyzer (NPB-DAA). For the purpose of developing NPB-DAA, this paper presents a novel two-layer hierarchical hidden semi-Markov model called hierarchical Dirichlet process hidden language model (HDP-HLM).

    2. Tadahiro Taniguchi, Ryo Nakashima, Hailong Liu and Shogo Nagasaka, Double Articulation Analyzer with Deep Sparse Autoencoder for Unsupervised Word Discovery from Speech Signals, Advanced Robotics, Vol.30 (), (11-12) pp. 770-783 .(2016) DOI:10.1080/01691864.2016.1159981 [LINK]

        • By composing NPB-DAA with deep sparse autoencoder (DSAE) whch is an unsupervised deep learning method, the NPB-DAA showed great performance in the task of unsupervised word discovery from Japaese vowel signals.

    3. Tadahiro Taniguchi, Shogo Nagasaka, Double Articulation Analyzer for Unsegmented Human Motion using Pitman-Yor Language model and Infinite Hidden Markov Model, 2011 IEEE/SICE International Symposium on System Integration, pp. 250 - 255 .(2011) [PDF]

        • This is the original paper of the old version of the DAA called the conventional DAA in the paper 1. This simply uses the sticky hierarchical Dirichlet process-hidden Markov model (sticky HDP-HMM) for segmentation of time-series data and the nested Pitman-Yor language model (NPYLM) for segmenting the letter sequences, i.e., concatenated latent state sequences, into words.