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1. 发明授权

US07050969B2 Distributed speech recognition with codec parameters 失效
公开(公告)号：US07050969B2
公开(公告)日：2006-05-23
申请号：US09995179
申请日：2001-11-27
申请人： Bhiksha Raj , Joshua Midgal , Rita Singh
发明人： Bhiksha Raj , Joshua Midgal , Rita Singh
IPC分类号： G10L19/04
CPC分类号： G10L15/30 , G10L15/02 , G10L19/00
摘要： A method extracts speech recognition features from a speech signal coded as a bitstream. First, the bitstream is decoded to recover linear predictive coding filter parameters, and a residual signal. Then, the linear predictive coding filter parameters and the residual signal are discriminatively combined into speech recognition features.

2. 发明授权

US08433892B2 Privacy-preserving probabilistic inference based on hidden Markov models 有权
标题翻译：基于隐马尔可夫模型的隐私保护概率推理
公开(公告)号：US08433892B2
公开(公告)日：2013-04-30
申请号：US13076410
申请日：2011-03-30
申请人： Shantanu Rane , Wei Sun , Manas A. Pathak , Bhiksha Raj
发明人： Shantanu Rane , Wei Sun , Manas A. Pathak , Bhiksha Raj
IPC分类号： H04L29/06
CPC分类号： H04L9/008 , G06N7/005 , H04L2209/46
摘要： A probability of an observation sequence stored at a client is evaluated securely with respect to a hidden Markov model (HMM) stored at a server. The server determines, for each state of the HMM, an encryption of a log-probability of a current element of the observation sequence. Determines, for each state of the HMM, an encryption of a log-summation of a product of a likelihood of the observation sequence based on a previous element of the observation sequence and a transition probability to the state of the HMM. Determines an encryption of a log-likelihood of the observation sequence for each state as a product of the encryption of a log-summation and an encryption of a corresponding log-probability of the current element of the observation sequence; and determines an encryption of the log-probability of the observation sequence based on the log-likelihood of the observation sequence for each state.
摘要翻译：相对于存储在服务器中的隐马尔可夫模型（HMM），安全地评估存储在客户端的观察序列的概率。对于HMM的每个状态，服务器确定观察序列的当前元素的对数概率的加密。确定对于HMM的每个状态，基于观测序列的先前元素和HMM状态的转移概率，对观测序列的可能性的乘积的对数加和进行加密。确定每个状态的观察序列的对数似然度的加密，作为对数求和的加密和观察序列的当前元素的相应对数概率的加密的乘积; 并且基于每个状态的观察序列的对数似然度来确定观察序列的对数概率的加密。

3. 发明申请

US20120254612A1 Privacy-Preserving Probabilistic Inference Based on Hidden Markov Models 有权
标题翻译：基于隐马尔可夫模型的隐私保护概率推理
公开(公告)号：US20120254612A1
公开(公告)日：2012-10-04
申请号：US13076410
申请日：2011-03-30
申请人： Shantanu Rane , Wei Sun , Manas A. Pathak , Bhiksha Raj
发明人： Shantanu Rane , Wei Sun , Manas A. Pathak , Bhiksha Raj
IPC分类号： G06N5/02 , H04L9/32
CPC分类号： H04L9/008 , G06N7/005 , H04L2209/46
摘要： A probability of an observation sequence stored at a client is evaluated securely with respect to a hidden Markov model (HMM) stored at a server. The server determines, for each state of the HMM, an encryption of a log-probability of a current element of the observation sequence. Determines, for each state of the HMM, an encryption of a log-summation of a product of a likelihood of the observation sequence based on a previous element of the observation sequence and a transition probability to the state of the HMM. Determines an encryption of a log-likelihood of the observation sequence for each state as a product of the encryption of a log-summation and an encryption of a corresponding log-probability of the current element of the observation sequence; and determines an encryption of the log-probability of the observation sequence based on the log-likelihood of the observation sequence for each state.
摘要翻译：相对于存储在服务器中的隐马尔可夫模型（HMM），安全地评估存储在客户端的观察序列的概率。对于HMM的每个状态，服务器确定观察序列的当前元素的对数概率的加密。确定对于HMM的每个状态，基于观测序列的先前元素和HMM状态的转移概率，对观测序列的可能性的乘积的对数加和进行加密。确定每个状态的观察序列的对数似然度的加密，作为对数求和的加密和观察序列的当前元素的相应对数概率的加密的乘积; 并且基于每个状态的观察序列的对数似然度来确定观察序列的对数概率的加密。

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