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It is well-known that the performance of the Gaussian Mixture Model (GMM) based Acoustic-to-Articulatory Inversion (AAI) improves by either incorporating smoothness constraint directly in the inversion criterion or smoothing (low-pass filtering) estimated articulator trajectories in a post-processing step, where smoothing is performed independently of the inversion. As the low-pass filtering is independent...
Techniques of video summarization have attracted significant research interests in the past decade due to the rapid progress in video recording, computation, and communication technologies. However, most of the existing methods analyze the video in an off-line manner, which greatly reduces the flexibility of the system. On-line summarization, which can progressively process video during video recording,...
Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks...
There has been growing interest in performing signal processing tasks directly on compressive measurements, e.g. low-dimensional linear measurements of signals taken with Gaussian random vectors. In this paper, we present a highly efficient algorithm to recover the covariance matrix of high-dimensional data from compressive measurements. We show that, as the number of data samples increases, the eigenvectors...
Recently, context-dependent Deep Neural Network (CD-DNN) has been found to significantly outperform Gaussian Mixture Model (GMM) for various large vocabulary continuous speech recognition tasks. Unlike the GMM approach, there is no meaningful interpretation of the DNN parameters, which makes it difficult to devise effective adaptation methods for DNNs. Furthermore, DNN parameter estimation is based...
We propose a speech enhancement algorithm that applies a Kalman filter in the modulation domain to the output of a conventional enhancer operating in the time-frequency domain. We show that the prediction residual signal of the spectral amplitude errors at the output of the baseline MMSE enhancer do not follow a Gaussian distribution. Accordingly, the Kalman filter used in our enhancement algorithm...
In this paper, we introduce Spear, an open source and extensible toolbox for state-of-the-art speaker recognition. This toolbox is built on top of Bob, a free signal processing and machine learning library. Spear implements a set of complete speaker recognition toolchains, including all the processing stages from the front-end feature extractor to the final steps of decision and evaluation. Several...
Electromagnetic Articulography (EMA) technique is used to record the kinematics of different articulators while one speaks. EMA data often contains missing segments due to sensor failure. In this work, we propose a maximum a-posteriori (MAP) estimation with continuity constraint to recover the missing samples in the articulatory trajectories recorded using EMA. In this approach, we combine the benefits...
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