141 research outputs found
Improvements of Hungarian Hidden Markov Model-based text-to-speech synthesis
Statistical parametric, especially Hidden Markov Model-based, text-to-speech (TTS) synthesis has received much attention recently. The quality of HMM-based speech synthesis approaches that of the state-of-the-art unit selection systems and possesses numerous favorable features, e.g. small runtime footprint, speaker interpolation, speaker adaptation. This paper presents the improvements of a Hungarian HMM-based speech synthesis system, including speaker dependent and adaptive training, speech synthesis with pulse-noise and mixed excitation. Listening tests and their evaluation are also described
Development of Efficient Drive Based on Self-help
The efficiency and the life rating are essential characteristics of mechanical drives. The traction drives with proper geometry can avoid the geometrical slip and their efficiency can exceed that of the gear drives. The elements has hardened steel surfaces, the lubricant is rheopectic. There is no danger for thinning the oil film and consequently for connecting the asperities. The traction drives are relatively noiseless, they are applicable for increasing speed in particular. There are some problems to be solved in friction drive. This is the necessity of clamping force. A simple machine element usually make a constant clamping force, a tensioning mechanism can be too complicated. The ideal solution is a simple design which assure a clamping force that is proportional to the instantaneous external load requirements. The authors suggest a modified machine element – a helical torsion spring, an elastic one, instead of the original, rigid annular wheel – that comprises both the driving and clamping functions, and the latter one is proportional to the external load, so that the principle of self-help operates
Multilingual statistical text analysis, Zipf's law and Hungarian speech generation
The practical challenge of creating a Hungarian e-mail reader has initiated our work on statistical text analysis. The starting point was statistical analysis for automatic discrimination of the language of texts. Later it was extended to automatic re-generation of diacritic signs and more detailed language structure analysis. A parallel study of three different languages-Hungarian, German and English-using text corpora of a similar size gives a possibility for the exploration of both similarities and differences. Corpora of publicly available Internet sources were used. The corpus size was the same (approximately 20 Mbytes, 2.5-3.5 million word forms) for all languages. Besides traditional corpus coverage, word length and occurrence statistics, some new features about prosodic boundaries (sentence initial and final positions, preceding and following a comma) were also computed. Among others, it was found that the coverage of corpora by the most frequent words follows a parallel logarithmic rule for all languages in the 40-85% coverage range, known as Zipf's law in linguistics. The functions are much nearer for English and German than for Hungarian. Further conclusions are also drawn. The language detection and diacritic regeneration applications are discussed in detail with implications on Hungarian speech generation. Diverse further application domains, such as predictive text input, word hyphenation, language modelling in speech recognition, corpus-based speech synthesis, etc. are also foreseen
SkálázhatĂł szöveg-alapĂş nyelvazonosĂtĂł mĂłdszer beszĂ©dszintĂ©zis cĂ©ljára
Szövegek nyelvĂ©nek automatikus azonosĂtása nagyon fontos több alkalmazásterĂĽleten. E cikkben áttekintjĂĽk a szövegbĹ‘l törtĂ©nĹ‘ nyelvazonosĂtása (language identification, LID) használt fĹ‘bb mĂłdszereket Ă©s leĂrjuk legfontosabb tulajdonságaikat. Ezek egyes, nagyon rövid szövegekre helyes kezelĂ©sĂ©t is igĂ©nylĹ‘ alkalmazásterĂĽleteken – mint pĂ©ldául a beszĂ©dszintĂ©zis – jelentkezĹ‘ hiányosságai kezelĂ©sĂ©re egy Ăşj mĂłdszert mutatunk be, amely változĂł hosszĂşságĂş N-gramok használatán alapulĂł, tisztán statisztikai mĂłdszer, emellett tetszĹ‘leges szöveg helyes azonosĂtására betanĂthatĂł, jĂłl skálázhatĂł, Ă©s viszonylag kis számĂtási kapacitást igĂ©nyel az azonosĂtási fázisban. Bemutatjuk hatĂ©konyságát a tanĂtĂł- Ă©s attĂłl fĂĽggetlen tesztanyagon, kĂĽlönbözĹ‘ mĂ©ret szövegtörzseken valĂł tanĂtás esetĂ©n, kevĂ©s Ă©s nagyon nagy számĂş nyelven valĂł mködĂ©s esetĂ©n is. Az eredmĂ©nyek igazolják a megközelĂtĂ©s Ă©letkĂ©pessĂ©gĂ©t
Multimodális kommunikáció alkalmazása projektvezetésben
Napjaink intenzĂv kommunikáciĂłra alapulĂł informáciĂłs társadalmában egyre nagyobb hangsĂşlyt kap a testreszabhatĂł, intelligens, rugalmas Ă©s alkalmazkodĂł informáciĂłtovábbĂtás, mely nem kötĹ‘dik csak egyetlen mĂ©diumhoz. Ez a törekvĂ©s Ă©rvĂ©nyes a projektvezetĂ©s tĂ©makörĂ©ben is, mely a projektkezelĹ‘ rendszerek multimodalitásában mutatkozik meg.
A PromĂłciĂł projekt keretein belĂĽl megvalĂłsĂtott projektkezelĹ‘ rendszer kĂ©pes a felhasználĂłk felĂ© irányulĂł kommunikáciĂł többalakĂş, többmĂłdĂş megvalĂłsĂtására. Ennek lĂ©nyege, hogy a felhasználĂł Ă©s a rendszer közötti információáramlásra nincs egyetlen dedikált csatorna (technolĂłgia). KözvetlenĂĽl az adatközvetĂtĂ©s elĹ‘tt döntĂ©s szĂĽletik a felhasználandĂł mĂ©diumrĂłl, mely lehet pĂ©ldául email (elektronikus levĂ©l), SMS (rövid szöveges ĂĽzenet) vagy akár hangĂĽzenet (telefonos hĂvás Ă©s Text-To-Speech motor ötvözĂ©sĂ©vel).
A felhasználĂłk felĂ© továbbĂtandĂł ĂĽzenetek absztrakt formában keletkeznek. A kĂ©zbesĂtĂ©s mĂłdja a projektkezelĹ‘ rendszerbe Ă©pĂtett logika alapján dĹ‘l el. Ha pĂ©ldául a felhasználĂł nem válaszol 10 percen belĂĽl egy emailre, akkor mobiltelefonon keresztĂĽl prĂłbáljuk elĂ©rni. Nappal a gyors reszponzivitás Ă©rdekĂ©ben hanghĂvással, Ă©jszaka viszont SMS-t használva.
A projektkezelĹ‘ rendszer multimodális megoldást alkalmaz a kĂ©rdĹ‘Ăvek kitöltĂ©sĂ©re is. A lĂ©trehozott kĂ©rdĹ‘Ăvek felhasználĂłhoz rendelhetĹ‘k, opcionálisan definiálhatĂł, hogy mely projekt mely feladatában válnak aktuálissá. A kĂ©rdĂ©sek megválaszolására a felhasználĂłi felĂĽleten kĂvĂĽl lehetĹ‘sĂ©g van telefonnal is, ez kĂĽlönösen hasznos offline (pĂ©ldául terepen dolgozĂł) felhasználĂłk esetĂ©n. A kĂ©rdĹ‘Ăvek XML formátumra alakĂtását követĹ‘en a Text-To-Speech motor beolvassa a kĂ©rdĂ©seket, Ă©s a telefonbillentyűk segĂtsĂ©gĂ©vel megadhatĂłk a válaszok, melyeket a rendszer eltárol
Order Creates Value: Personality, Attitudinal and Behavioral Factors of Financial Vulnerability
El orden crea valor: Factores de personalidad, actitud y comportamiento de la vulnerabilidad financieraEn nuestra investigaciĂłn, nuestro objetivo es conocer las actitudes y comportamientos financieros de los grupos sociales econĂłmicamente frágiles. Con base en una encuesta de cuestionario en lĂnea (N = 22933 adultos), formamos grupos mediante análisis de conglomerados y los comparamos entre sĂ. Examinamos los grupos con mĂ©todos estadĂsticos multivariables, destacando las caracterĂsticas relacionadas con la vulnerabilidad financiera. Además, desarrollamos la mĂ©trica de fragilidad financiera. Los resultados llaman la atenciĂłn sobre el hecho de que la fragilidad financiera tiene mĂşltiples razones complejas e interrelacionadas. En tĂ©rminos de personalidad financiera, actitud y comportamiento, alcanzaron el punto promedio más alto en el caso de reclamos claramente adversos, mientras que el más bajo en reclamos benĂ©ficos. Nuestros resultados demuestran que, incluso si el grupo financieramente vulnerable representa el 9 %, el grupo de los 'pozos de dinero' y los 'pasivos' muestran varias actitudes y patrones de comportamiento que podrĂan conducir a la fragilidad financiera en el futuro. La proporciĂłn acumulada de estos grupos asciende al 32%. Las personas econĂłmicamente vulnerables no cuidan bien ni sus finanzas ni su hogar, no pueden planificar ni prolongar sus deseos, y juzgan incurable su situaciĂłn, a lo que se suma la ansiedad.In our research, we aim to become acquainted with the attitudes and financial behaviours of financially fragile social groups. Based on an online questionnaire survey (N=22933 adult), we formed groups by cluster analysis and compared them to each other. We examined the groups with multivariable statistical methods, underscoring the characteristics relating to financial vulnerability. Beside we developed the metric for financial fragility. The results draw attention that financial fragility has multiple, complex, interrelated reasons. In terms of financial personality, attitude, and behaviour, they reached the highest average point in case of distinctly adverse claims, while the lowest at beneficial claims. Our results demonstrate that even if the financially vulnerable cluster accounts for 9%, the group of 'money pits' and the 'passive' show several such attitudes and behavioural patterns that could lead to financial fragility in the future. The cumulative ratio of these groups amounts to 32%. Financially vulnerable people do not take good care of either their finances or their household, they can't plan or prolong their wishes, and they judge their situation incurable, which is coupled with anxiety
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