%0 Conference Paper %1 Hunt1996 %A Hunt, Andrew J. %A Black, Alan W. %B Proceedings of the 1996 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) %C Atlanta, GA, USA %D 1996 %K Viterbi algorithm context cost;state cost;waveform database;natural database;training;transition decoding;concatenative decoding;search estimation;Viterbi information;prosodic information;pruned languages;Network network;synthesis occupancy problems;speech recognition;Speech search;state sequence;phonetic speech speech;natural-sounding speech;phoneme synthesis synthesis;Control synthesis;Costs;Databases;Laboratories;Natural synthesis;Speech synthesis;State synthesis;Viterbi synthesized system system;database transition unit;large %P 373-376 %R 10.1109/ICASSP.1996.541110 %T Unit selection in a concatenative speech synthesis system using a large speech database %V 1 %X One approach to the generation of natural-sounding synthesized speech waveforms is to select and concatenate units from a large speech database. Units (in the current work, phonemes) are selected to produce a natural realisation of a target phoneme sequence predicted from text which is annotated with prosodic and phonetic context information. We propose that the units in a synthesis database can be considered as a state transition network in which the state occupancy cost is the distance between a database unit and a target, and the transition cost is an estimate of the quality of concatenation of two consecutive units. This framework has many similarities to HMM-based speech recognition. A pruned Viterbi search is used to select the best units for synthesis from the database. This approach to waveform synthesis permits training from natural speech: two methods for training from speech are presented which provide weights which produce more natural speech than can be obtained by hand-tuning

@inproceedings{Hunt1996, abstract = {One approach to the generation of natural-sounding synthesized speech waveforms is to select and concatenate units from a large speech database. Units (in the current work, phonemes) are selected to produce a natural realisation of a target phoneme sequence predicted from text which is annotated with prosodic and phonetic context information. We propose that the units in a synthesis database can be considered as a state transition network in which the state occupancy cost is the distance between a database unit and a target, and the transition cost is an estimate of the quality of concatenation of two consecutive units. This framework has many similarities to HMM-based speech recognition. A pruned Viterbi search is used to select the best units for synthesis from the database. This approach to waveform synthesis permits training from natural speech: two methods for training from speech are presented which provide weights which produce more natural speech than can be obtained by hand-tuning}, added-at = {2021-02-01T10:51:23.000+0100}, address = {Atlanta, GA, USA}, author = {Hunt, Andrew J. and Black, Alan W.}, biburl = {https://www.bibsonomy.org/bibtex/215558da362f59d7fdd21eb4a7768b0b4/m-toman}, booktitle = {Proceedings of the 1996 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, doi = {10.1109/ICASSP.1996.541110}, file = {:pdfs/hunt_icassp_1996.pdf:PDF}, interhash = {b1658aa9854d8ac5f504120525b280a4}, intrahash = {15558da362f59d7fdd21eb4a7768b0b4}, issn = {1520-6149}, keywords = {Viterbi algorithm context cost;state cost;waveform database;natural database;training;transition decoding;concatenative decoding;search estimation;Viterbi information;prosodic information;pruned languages;Network network;synthesis occupancy problems;speech recognition;Speech search;state sequence;phonetic speech speech;natural-sounding speech;phoneme synthesis synthesis;Control synthesis;Costs;Databases;Laboratories;Natural synthesis;Speech synthesis;State synthesis;Viterbi synthesized system system;database transition unit;large}, month = may, owner = {schabus}, pages = {373-376}, timestamp = {2021-02-01T10:51:23.000+0100}, title = {Unit selection in a concatenative speech synthesis system using a large speech database}, volume = 1, year = 1996 }