A comparison between STRAIGHT, glottal, and sinusoidal vocoding in statistical parametric speech synthesis
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A comparison between STRAIGHT, glottal, and sinusoidal vocoding in statistical parametric speech synthesis. / Airaksinen, Manu; Juvela, Lauri; Bollepalli, Bajibabu; Yamagishi, Junichi; Alku, Paavo.
julkaisussa: IEEE/ACM Transactions on Audio Speech and Language Processing, Vuosikerta 26, Nro 9, 09.2018, s. 1658-1670.Tutkimustuotos: Lehtiartikkeli › › vertaisarvioitu
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TY - JOUR
T1 - A comparison between STRAIGHT, glottal, and sinusoidal vocoding in statistical parametric speech synthesis
AU - Airaksinen, Manu
AU - Juvela, Lauri
AU - Bollepalli, Bajibabu
AU - Yamagishi, Junichi
AU - Alku, Paavo
PY - 2018/9
Y1 - 2018/9
N2 - A vocoder is used to express a speech waveform with a controllable parametric representation that can be converted back into a speech waveform. Vocoders representing their main categories (mixed excitation, glottal, sinusoidal vocoders) were compared in this study with formal and crowd-sourced listening tests. Vocoder quality was measured within the context of analysis-synthesis as well as text-to-speech (TTS) synthesis in a modern statistical parametric speech synthesis framework. Furthermore, the TTS experiments were divided into synthesis with vocoder-specific features and synthesis with a shared envelope model, where the waveform generation method of the vocoders is mainly responsible for the quality differences. Finally, all of the tests included four distinct voices as a way to investigate the effect of different speakers on the synthesized speech quality. The obtained results suggest that the choice of the voice has a profound impact on the overall quality of the vocoder-generated speech, and the best vocoder for each voice can vary case by case. The single best-rated TTS system was obtained with the glottal vocoder GlottDNN using a male voice with low expressiveness. However, the results indicate that the sinusoidal vocoder PML (pulse model in log-domain) has the best overall performance across the performed tests. Finally, when controlling for the spectral models of the vocoders, the observed differences are similar to the baseline results. This indicates that the waveform generation method of a vocoder is essential for quality improvements.
AB - A vocoder is used to express a speech waveform with a controllable parametric representation that can be converted back into a speech waveform. Vocoders representing their main categories (mixed excitation, glottal, sinusoidal vocoders) were compared in this study with formal and crowd-sourced listening tests. Vocoder quality was measured within the context of analysis-synthesis as well as text-to-speech (TTS) synthesis in a modern statistical parametric speech synthesis framework. Furthermore, the TTS experiments were divided into synthesis with vocoder-specific features and synthesis with a shared envelope model, where the waveform generation method of the vocoders is mainly responsible for the quality differences. Finally, all of the tests included four distinct voices as a way to investigate the effect of different speakers on the synthesized speech quality. The obtained results suggest that the choice of the voice has a profound impact on the overall quality of the vocoder-generated speech, and the best vocoder for each voice can vary case by case. The single best-rated TTS system was obtained with the glottal vocoder GlottDNN using a male voice with low expressiveness. However, the results indicate that the sinusoidal vocoder PML (pulse model in log-domain) has the best overall performance across the performed tests. Finally, when controlling for the spectral models of the vocoders, the observed differences are similar to the baseline results. This indicates that the waveform generation method of a vocoder is essential for quality improvements.
KW - Acoustics
KW - Predictive models
KW - Production
KW - Speech synthesis
KW - statistical parametric speech synthesis
KW - Transfer functions
KW - vocoder
KW - Vocoders
UR - http://www.scopus.com/inward/record.url?scp=85046811905&partnerID=8YFLogxK
U2 - 10.1109/TASLP.2018.2835720
DO - 10.1109/TASLP.2018.2835720
M3 - Article
VL - 26
SP - 1658
EP - 1670
JO - IEEE/ACM Transactions on Audio, Speech, and Language Processing
JF - IEEE/ACM Transactions on Audio, Speech, and Language Processing
SN - 2329-9290
IS - 9
ER -
ID: 21298817