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Adaptive Kalman filtering and smoothing for tracking vocal tract resonances using a continuousvalued hidden dynamic model

机译：使用连续值隐藏动态模型的自适应卡尔曼滤波和平滑跟踪声道共振

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Li Deng; Leo J. Lee; Hagai Attias; Alex Acero;
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年度 2007
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1. Adaptive Kalman Filtering and Smoothing for Tracking Vocal Tract Resonances Using a Continuous-Valued Hidden Dynamic Model [J] . Li Deng, Leo J. Lee, Hagai Attias, IEEE transactions on audio, speech and language processing . 2007,第1期

机译：自适应卡尔曼滤波和平滑法，用于跟踪人声共振，采用连续值隐藏动态模型
2. Cascade Prediction Filters With Adaptive Zeros to Track the Time-Varying Resonances of the Vocal Tract [J] . Vargas J., McLaughlin S. IEEE transactions on audio, speech and language processing . 2008,第1期

机译：具有自适应零点的级联预测滤波器，用于跟踪人声道的时变共振
3. Dynamic Speech Spectrum Representation and Tracking Variable Number of Vocal Tract Resonance Frequencies With Time-Varying Dirichlet Process Mixture Models [J] . Ozkan E., Ozbek I.Y., Demirekler M. Audio, Speech, and Language Processing, IEEE Transactions on . 2009,第8期

机译：时变Dirichlet过程混合模型的动态语音频谱表示和声道共振频率的可变数目跟踪
4. Vocal tract resonances tracking based on voiced and unvoiced speech classification using dynamic programming and fixed interval kalman smoother [C] . Ozbek, I.Y., Demirekler, Personal, Indoor and Mobile Radio Communications,2005 IEEE 16th International Symposium on . 2008

机译：使用动态规划和固定间隔卡尔曼平滑器，基于有声和无声语音分类进行声场共振跟踪
5. Adaptive multiband Kalman filtering for target tracking. [D] . Hung, Yung-Shung. 1991

机译：自适应多频带卡尔曼滤波，用于目标跟踪。
6. Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography [O] . Zhenrui Chen, Yanmei Tie, Olutayo Olubiyi, -1

机译：皮质脊髓束建模用于神经外科计划方法是使用两张无味卡尔曼滤镜进行追踪肿瘤周围水肿和交叉纤维区域
7. A Structured Speech Model with Continuous Hidden Dynamics and Prediction-Residual Training for Tracking Vocal Tract Resonances [O] . Li Deng, Leo J. Lee, Hagai Attias, 2004

机译：具有连续隐藏动力学和预测残差训练的结构化语音模型用于跟踪声带共振

Adaptive Kalman filtering and smoothing for tracking vocal tract resonances using a continuousvalued hidden dynamic model

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