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—The Internet Protocol (IP) environment poses two relevant sources of distortion to the speech recognition problem: lossy speech coding and packet loss. In this paper, we propose a new front-end for speech recognition over IP networks. Specifically, we suggest extracting the recognition feature vectors directly from the encoded speech (i.e., the bit stream)(More)
Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them(More)
We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in real-world scenarios, a model selection scheme is also proposed.
Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers could provide an appropriate solution, since they are very well adapted to high-dimensional classification(More)
—In this paper, we propose to quantify the quality of the recorded voice through objective nonlinear measures. Quan-tification of speech signal quality has been traditionally carried out with linear techniques since the classical model of voice production is a linear approximation. Nevertheless, nonlinear behaviors in the voice production process have been(More)
In this paper, we have extended our previous research on a new approach to ASR in the GSM environment. Instead of recognizing from the decoded speech signal, our system works from the digital speech representation used by the GSM encoder. We have compared the performance of a conventional system and the one we propose on a speaker independent,(More)
We propose a complete video encoder based on directional " non-separable " transforms that allow spatial and temporal correlation to be jointly exploited. These lifting-based wavelet transforms are applied on graphs that link pixels in a video sequence based on motion information. In this paper, we first consider a low complexity version of this transform,(More)
In this paper we propose the use of nonlinear speech features to improve the voice quality measurement. We have tested a couple of features from the Dynamical System Theory, namely: the Correlation Dimension and the largest Lyapunov Exponent. In particular, we have studied the optimal size of time window for this type of analysis in the field of the(More)