James G. Droppo
Microsoft
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by James G. Droppo.
international conference on acoustics, speech, and signal processing | 2004
James G. Droppo; Alejandro Acero
Model based feature enhancement techniques are constructed from acoustic models for speech and noise, together with a model of how the speech and noise produce the noisy observations. Most techniques incorporate either Gaussian mixture models (GMM) or hidden Markov models (HMM). This paper explores using a switching linear dynamic model (LDM) for the clean speech. The linear dynamics of the model capture the smooth time evolution of speech. The switching states of the model capture the piecewise stationary characteristics of speech. However, incorporating a switching LDM causes the enhancement problem to become intractable. With a GMM or an HMM, the enhancement running time is proportional to the length of the utterance. The switching LDM causes the running time to become exponential in the length of the utterance. To overcome this drawback, the standard generalized pseudo-Bayesian technique is used to provide an approximate solution of the enhancement problem. We present preliminary results demonstrating that, even with relatively small model sizes, substantial word error rate improvement can be achieved.
Journal of the Acoustical Society of America | 2006
James G. Droppo; Alejandro Acero; Li Deng
A method and apparatus are provided for identifying a noise environment for a frame of an input signal based on at least one feature for that frame. Under one embodiment, the noise environment is identified by determining the probability of each of a set of possible noise environments. For some embodiments, the probabilities of the noise environments for past frames are included in the identification of an environment for a current frame. In one particular embodiment, a count is generated for each environment that indicates the number of past frames for which the environment was the most probable environment. The environment with the highest count is then selected as the environment for the current frame.
Journal of the Acoustical Society of America | 2012
Alejandro Acero; James G. Droppo; Milind Mahajan
Parameters for a feature extractor and acoustic model of a speech recognition module are trained. An objective function is utilized to determine values for the feature extractor parameters and the acoustic model parameters.
Archive | 2004
Zicheng Liu; Michael J. Sinclair; Alejandro Acero; Xuedong Huang; James G. Droppo; Li Deng; Zhengyou Zhang; Yanli Zheng
Archive | 2002
Alejandro Acero; Li Deng; James G. Droppo
Archive | 1998
Alejandro Acero; James G. Droppo
Journal of the Acoustical Society of America | 2007
Li Deng; James G. Droppo; Alejandro Acero
Archive | 2004
James G. Droppo; Alejandro Acero; Li Deng
Archive | 2002
Alejandro Acero; Li Deng; James G. Droppo
Archive | 2004
Jian Wu; James G. Droppo; Li Deng; Alejandro Acero