András Bánhalmi

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For One-Class Classification problems several methods have been proposed in the literature. These methods all have the common feature that the decision boundary is learnt by just using a set of the positive examples. Here we propose a method that extends the training set with a counterexample set, which is generated directly using the set of positive(More)
Prevention and rehabilitation efficiency can greatly benefit from the application of intelligent, 24 hour tele-diagnostics and tele-care information systems. Tele-monitoring also supports a new level of medical supervision over the patient's lifestyle. In this paper we briefly present the architecture and development phase results of the Alpha remote(More)
In the past few years numerous techniques have been proposed to improve the efficiency of basic adaptation methods like MLLR and MAP. These adaptation methods have a common aim, which is to increase the likelihood of the phoneme models for a particular speaker. During their operation, these speaker adaptation methods need precise phonetic segmentation(More)
This paper proposes a pitch estimation algorithm that is based on optimal harmonic model fitting. The algorithm operates directly on the time-domain signal and has a relatively simple mathematical background. To increase its efficiency and accuracy, the algorithm is applied in combination with an autocorrelation-based initialization phase. For testing(More)
Our aim is to implement a plant identification application that can run on smartphones, and this shared task includes it. After the plant identification task of 2013 we concluded that the most frequent trees (e. g. in Hungary) can be identified well by a leaf, when there is a white paper background behind it at the time of photographing. This is why we want(More)
In the therapy of the hearing impaired one of the key problems is how to deal with the lack of proper auditive feedback which impedes the development of intelligible speech. The effectiveness of the therapy relies heavily on accurate phoneme recognition. Because of the environmental difficulties, simple recognition algorithms may have a weak classification(More)
When training speaker-independent HMM-based acoustic models, a lot of manually transcribed acoustic training data must be available from a good many different speakers. These training databases have a great variation in the pitch of the speakers, articulation and the speed of talking. In practice, the speaker-independent models are used for bootstrapping(More)