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Advanced Algorithms and Architectures for Speech by Giancarlo Pirani (auth.), Giancarlo Pirani (eds.) PDF

By Giancarlo Pirani (auth.), Giancarlo Pirani (eds.)

ISBN-10: 3540534024

ISBN-13: 9783540534020

ISBN-10: 3642843417

ISBN-13: 9783642843419

This ebook is meant to provide an summary of the foremost effects completed within the box of average speech realizing within ESPRIT undertaking P. 26, "Advanced Algorithms and Architectures for Speech and photo Processing". The venture started as a Pilot undertaking within the early level of section 1 of the ESPRIT software introduced via the fee of the ecu groups. After twelve months, within the gentle of the initial effects that have been got, it was once proven for its 5-year length. although the actions have been conducted for either speech and photo comprehend­ ing we most popular to concentration the therapy of the ebook at the first quarter which crystallized quite often round the CSELT staff, with the dear cooperation of AEG, Thomson-CSF, and Politecnico di Torino. as a result paintings of the 5 years of the undertaking, the Consortium used to be capable of boost a precise and whole figuring out process that is going from a consistently spoken average language sentence to its which means and the ensuing entry to a database. after we began in 1983 we had a few services in small-vocabulary syntax-driven connected-word speech acceptance utilizing Hidden Markov types, in written traditional lan­ guage realizing, and in layout as a rule dependent upon bit-slice microprocessors.

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Extra resources for Advanced Algorithms and Architectures for Speech Understanding

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Similar considerations lead to a third heuristic (H3) that exploits robust cues for deciding that a particular phonetic class cannot be hypothesized for a given segment. If phonetic class k.. cannot definitely be assigned to a micro-segment, it cannot be substituted in the 3DP matching for a symbol of the representation alphabet belonging to class kn . Frame energy, for example, has been used as a cue for deciding that a high energy micro-segment cannot be substituted for a plosive sound. 11 shows the performance obtained by using the H2 and H3 heuristics, and a 5-frame window majority voting filter (Ht).

T, the COST array stores the cost values of the current best alignment paths ending in node TN(i) after micro-segments M(t) and M(t + 1) have been observed. The DECS array stores the labels of micro-segments M( t) and M( t + 1) that are drawn by the optimization process; this information is used for dealing with "false insertion" events. Links to nodes that must be expanded are held into the LINK array. 9 . When the last micro-segment M(T) is observed, the active nodes in list L( current) are processed only for possible extension of the best paths through deletions.

These 7735 utterances were automatically labeled in terms of phonetic units as will be described in Sect. 5, where training of HMMs is illustrated, and used for estimating the parameters of 7 speaker-dependent classifiers. Another set of 1011 words, belonging to the dictionary of the geographic data base_ query application (GEO), was recorded by the same speakers and all the tests were performed on this set of 7077 utterances. 6. 6 considers also the possible alternative decision. In 63% of the cases a single label is assigned to a frame.

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Advanced Algorithms and Architectures for Speech Understanding by Giancarlo Pirani (auth.), Giancarlo Pirani (eds.)

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