Please use this identifier to cite or link to this item: doi:10.22028/D291-25179
Title: Robust pitch period detection using dynamic programming with an ANN cost function
Author(s): Harbeck, Stefan
Kießling, Andreas
Kompe, Ralf
Niemann, Heinrich
Nöth, Elmar
Language: English
Year of Publication: 1995
OPUS Source: Saarbrücken, 1995
SWD key words: Künstliche Intelligenz
DDC notations: 004 Computer science, internet
Publikation type: Report
Abstract: In this paper, a new pitch synchronous F0-algorithm is described. The task of detecting pitch periods in the speech signal is solved with a search for an optimal path through a space of pitch period hypotheses. The search is efficiently implemented by dynamic programming (DP). The DP cost function is computed with an automatically trained artificial neural network (ANN) which combines the outputs of heuristic functions measuring the similarity of adjacent period hypotheses. With this algorithm a coarse error rate of 4,75% on a German speech database is achieved. It outperforms the DPF algorithm, which itselfs outperforms two "conventional'; algorithms.
Link to this record: urn:nbn:de:bsz:291-scidok-41672
Series name: Vm-Report / Verbmobil, Verbundvorhaben, [Deutsches Forschungszentrum für Künstliche Intelligenz]
Series volume: 91
Date of registration: 5-Sep-2011
Faculty: SE - Sonstige Einrichtungen
Department: SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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