50 citations · 75 across the 26 of their papers we have counts for
6 papers · 1 filter
Deep Learning for Tube Amplifier Emulation
Eero-Pekka Damskägg, Lauri Juvela, Etienne Thuillier +1
Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog…
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks
Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi +1
The state-of-the-art in text-to-speech synthesis has recently improved considerably due to novel neural waveform generation methods, such as WaveNet. However, these methods suffer…
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention
Bajibabu Bollepalli, Lauri Juvela, Paavo Alku
Currently, there are increasing interests in text-to-speech (TTS) synthesis to use sequence-to-sequence models with attention. These models are end-to-end meaning that they learn b…
Speaker-independent raw waveform model for glottal excitation
Lauri Juvela, Vassilis Tsiaras, Bajibabu Bollepalli +3
Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i.e., generating speech waveforms from acoustic features. These models have…
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
Xin Wang, Jaime Lorenzo-Trueba, Shinji Takaki +2
Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in ac…
Speech waveform synthesis from MFCC sequences with generative adversarial networks
Lauri Juvela, Bajibabu Bollepalli, Xin Wang +4
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are ge…