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Gender-dependent (male/female) acoustic models are more acoustically homogeneous and therefore give better recognition performance than single gender-independent model. This paper deals with a problem how to use these gender-based acoustic models in a real-time LVCSR (Large Vocabulary Continuous Speech Recognition) system that is for more than one year used(More)
Gaussian Mixture Models (GMMs) are widely used among scientists e.g. in statistics toolkits and data mining procedures. In order to estimate parameters of a GMM the Maximum Likelihood (ML) training is often utilized, more precisely the Expectation-Maximization (EM) algorithm. Nowadays, a lot of tasks works with huge datasets, what makes the estimation(More)
The main goal of this paper is to explore the methods of genderdependent acoustic modeling that would take the possibly of imperfect function of a gender detector into consideration. Such methods will be beneficial in realtime recognition tasks (eg. real-time subtitling of meetings) when the automatic gender detection is delayed or incorrect. The goal is to(More)
In this paper we present a highly optimized implementation of Gaussian mixture acoustic model evaluation algorithm. Evaluation of these likelihoods is one of the most computationally intensive parts of automatics speech recognizers but it can be well-parallelized and offloaded to GPU devices. Our approach offers significant speed-up compared to the recently(More)
Gaussian mixture models (GMMs) are often used in various data processing and classification tasks to model a continuous probability density in a multi-dimensional space. In cases, where the dimension of the feature space is relatively high (e.g. in the automatic speech recognition (ASR)), GMM with a higher number of Gaussians with diagonal covariances (DC)(More)
The main objective of the work presented in this paper was to develop a complete system that would accomplish the original visions of the MALACH project. Those goals were to employ automatic speech recognition and information retrieval techniques to provide improved access to the large video archive containing recorded testimonies of the Holocaust(More)
In this paper, we describe an optimized version of a Gaussian-mixture-based acoustic model likelihood evaluation algorithm for graphical processing units (GPUs). The evaluation of these likelihoods is one of the most computationally intensive parts of automatic speech recognizers, but it can be parallelized and offloaded to GPU devices. Our approach offers(More)
UNLABELLED Mass spectrometers are sophisticated, fine instruments which are essential in a variety applications. However, the data they produce are usually interpreted in a rather primitive way, without considering the accuracy of this data and the potential errors in identifying peaks. Our new approach corrects this situation by dividing the LC-MS output(More)
The article introduces an expert system for the speaker verification task. Our main purpose was to design a tool for the combination of various speaker verification systems proposed for various operating conditions. First of all, the essential ideas are explained that made us design the expert system. Next section describes the structure of a rule-based(More)