Máté Szarvas

Learn More
This paper presents a novel pedestrian detection method based on the use of a convolutional neural network (CNN) classifier. Our method achieves high accuracy by automatically optimizing the feature representation to the detection task and regularizing the neural network. We evaluate the proposed method on a difficult database containing pedestrians in a(More)
This paper presents a novel real-time pedestrian detection system utilizing a LIDAR-based object detector and convolutional neural network (CNN)-based image classifier. Our method achieves over 10 frames/second processing speed by constraining the search space using the range information from the LIDAR. The image region candidates detected by the LIDAR are(More)
This article introduces a novel approach to model morphosyntax in morpheme unit based speech recognizers. The proposed method is evaluated in our recent Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the weighted finite state transducer (WFST) paradigm. The task domain is the(More)
This article describes the design and the experimental evaluation of the first Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the recently proposed weighted finite state transducer (WFST) paradigm. The task domain is the recognition of fluently read sentences selected from a(More)
In this article we evaluate our stochastic morphosyntactic language model (SMLM) on a Hungarian newspaper dictation task that requires modeling over 1 million different word forms. The proposed method is based on the use of morphemes as the basic recognition units and the combination of a morpheme AE-gram model and a morphosyntactic language model. The(More)
This paper describes a novel method that models the correlation between acoustic observations in contiguous speech segments. The basic idea behind the method is that acoustic observations are conditioned not only on the phonetic context but also on the preceding acoustic segment observation. The correlation between consecutive acoustic observations is(More)