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Blind source separation can be implemented in the frequency domain using one-tap multiplication operation in each frequency bin, but only when the frame length is long enough to disregard temporal aliasing effects. If we take a short-time frequency transformation with a window shorter than a room reverberation time, the justification above does not hold anymore. In this paper, we present an appropriate...
In this paper, we propose a multi-microphone joint optimal estimation of the direction of arrival (DOA) and the source speech signal through newly introduced EM beamforming. This produces a posterior PDF for the DOA, based only on the reliable speech spectrum. By maximizing over the posterior PDF of the DOA, we achieve maximum a posteriori DOA estimation. After convergence, the estimated source spectrum...
One important class of state emission densities of the hiddenMarkov model (HMM) is the Gaussian mixture densities. The classical Baum-Welch algorithm often fails to reliably learn the Gaussian mixture densities when there is insufficient training data, due to the large number of free parameters present in the model. In this paper, we propose a novel strategy for robustly and accurately learning the...
While a sound spoken is described by a handful of frame-level spectral vectors, not all frames have equal contribution for either human perception or machine classification. In this paper, we introduce a novel framework to automatically emphasize important speech frames relevant to phonetic information. We jointly learn the importance of speech frames by a distance metric across the phone classes,...
Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the expectation maximization (EM) algorithm based on a training data set. Then, classification is performed...
We present a system that detects human falls in the home environment, distinguishing them from competing noise, by using only the audio signal from a single far-field microphone. The proposed system models each fall or noise segment by means of a Gaussian mixture model (GMM) supervector, whose Euclidean distance measures the pairwise difference between audio segments. A support vector machine built...
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of features, and then a global Gaussian Mixture Model (GMM) learned from all images is used to randomly distribute each feature into one Gaussian component by a multinomial trial. The parameters of the multinomial distribution are...
Recent studies in patch-based Gaussian Mixture Model (GMM) approaches for face age estimation present promising results. We propose using a hidden Markov model (HMM) supervector to represent face image patches, to improve from the previous GMM supervector approach by capturing the spatial structure of human faces and loosening the assumption of identical face patch distribution within a face image...
In this paper, we propose a complete pipeline of efficient and low-cost techniques to construct a realistic 3D text-driven emotive audio-visual avatar from a single 2D frontal-view face image of any person on the fly. This real-time conversion is achieved through three steps. First, a personalized 3D face model is built based on the 2D face image using a fully automatic 3D face shape and texture reconstruction...
In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of orderless coordinate patches, the global distribution of which is described by Gaussian mixture models (GMM), and then each image is further expressed as a specific distribution model by Maximum a Posteriori adaptation from the...
Speech perceptual features, such as Mel-frequency Cepstral Coefficients (MFCC), have been widely used in acoustic event detection. However, the different spectral structures between speech and acoustic events degrade the performance of the speech feature sets. We propose quantifying the discriminative capability of each feature component according to the approximated Bayesian accuracy and deriving...
This paper proposes minimum mean squared error (MMSE) speech signal estimation in a reverberant space using different optimal estimators in the low and high frequency ranges. At low frequencies, an MMSE spectral amplitude estimator divided by the spectral amplitude of a representative impulse response produces optimal performance. In the high frequency range, the MMSE estimator is computed based on...
Emotive audio-visual avatars have the potential of significantly improving the quality of Human-Computer Interaction (HCI). In this paper, the various technical approaches of a novel framework leading to a text-driven 3D Emotive Audio-Visual Avatar (EAVA) are proposed. Primary work is focused on 3D face modeling, realistic emotional facial expression animation, emotive speech synthesis, and the co-articulation...
Emotive audio-visual avatars are virtual computer agents which have the potential of improving the quality of human-machine interaction and human-human communication significantly. However, the understanding of human communication has not yet advanced to the point where it is possible to make realistic avatars that demonstrate interactions with natural-sounding emotive speech and realistic-looking...
This paper proposes a multi-stream approach to automatic audiovisual speech recognition, based in part on Hickok and Poeppel's dual-stream model of human speech processing. The dual-stream model proposes that semantic networks may be accessed by at least three parallel neural streams: at least two ventral streams that map directly from acoustics to words (with different time scales), and at least...
Speaker verification is a technology of verifying the claimed identity of a speaker based on the speech signal from the speaker (voice print). To learn the score of similarity between each pair of target and trial utterances, we investigated two different discriminative learning frameworks: Fisher mapping followed by SVM learning and utterance transform followed by iterative cohort modeling (ICM)...
Mismatch between training and testing data is a major error source for both automatic speech recognition (ASR) and automatic speaker identification (ASI). In this paper, we first present a statistical weighting concept to exploit the unequal sensitivity of mel-frequency cepstral coefficients (MFCC) components to against the mismatch, such as ambient noise, recording equipment, transmission channels,...
We report on investigations, conducted at the 2006 Johns Hopkins Workshop, into the use of articulatory features (AFs) for observation and pronunciation models in speech recognition. In the area of observation modeling, we use the outputs of AF classifiers both directly, in an extension of hybrid HMM/neural network models, and as part of the observation vector, an extension of the "tandem"...
This paper studies the speech of three talkers with spastic dysarthria caused by cerebral palsy. All three subjects share the symptom of low intelligibility, but causes differ. First, all subjects tend to reduce or delete word-initial consonants; one subject deletes all consonants. Second, one subject exhibits a painstaking stutter. Two algorithms were used to develop automatic isolated digit recognition...
A theoretical basis for optimal multichannel speech enhancements presented, sufficient, flexible to be used with any assumed statistical model and optimality criterion. Any Bayesian optimal one-channel estimator for speech enhancement can be generalized to the multichannel case as a sequentially constructed minimum variance distortionless response (MVDR) beamformer followed by an optimal one-channel...
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