TY - GEN
T1 - Evolutionary process indicators for active IGAs applied to weight tuning in unit selection TTS synthesis
AU - Formiga, Lluís
AU - Alías, Francesc
AU - Llorà, Xavier
PY - 2010
Y1 - 2010
N2 - Text-to-Speech (TTS) synthesis systems produce speech from an input text. Corpus based or unit selection TTS (US-TTS) are based on retrieving the best set of speech units from a large labelled speech database. To that effect, the unit selection is guided by dynamic programming and a weighted cost function. Several weight tuning approaches have been defined so as to integrate human preferences in the unit selection process, but with no great success beyond expert-based hand tuning. However, active interactive genetic algorithms (aiGAs) have showed promising results working on the unit selection text-to-speech (US-TTS) weight tuning problem in previous works. aiGAs are an evolution of classic interactive genetic algorithms (IGAs) in terms of reducing fatigue, ambiguity and frustration in user's evaluations. This paper presents a step further in the application of aiGAs to this problem by defining new indicators of the perceptually-based evolutionary process to obtain more reliable weights. The experiments have been conducted on one hour Spanish speech database and using an acoustic plus linguistic cost function.
AB - Text-to-Speech (TTS) synthesis systems produce speech from an input text. Corpus based or unit selection TTS (US-TTS) are based on retrieving the best set of speech units from a large labelled speech database. To that effect, the unit selection is guided by dynamic programming and a weighted cost function. Several weight tuning approaches have been defined so as to integrate human preferences in the unit selection process, but with no great success beyond expert-based hand tuning. However, active interactive genetic algorithms (aiGAs) have showed promising results working on the unit selection text-to-speech (US-TTS) weight tuning problem in previous works. aiGAs are an evolution of classic interactive genetic algorithms (IGAs) in terms of reducing fatigue, ambiguity and frustration in user's evaluations. This paper presents a step further in the application of aiGAs to this problem by defining new indicators of the perceptually-based evolutionary process to obtain more reliable weights. The experiments have been conducted on one hour Spanish speech database and using an acoustic plus linguistic cost function.
UR - http://www.scopus.com/inward/record.url?scp=79959481496&partnerID=8YFLogxK
U2 - 10.1109/CEC.2010.5586131
DO - 10.1109/CEC.2010.5586131
M3 - Conference contribution
AN - SCOPUS:79959481496
SN - 9781424469109
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Y2 - 18 July 2010 through 23 July 2010
ER -