6 accepted papers
Recently developed neural-based TTS models have focused on robustness and finer control over acoustic features such as phoneme duration, energy, and F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf>, allowing users to have some degree of control…
We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. Our model builds upon disentanglement strategies proposed in RADMMM[1] and supports explicit control of accent, language, speaker and fine-grained F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xml…
Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. However, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in c…
Non-intrusive estimators for acoustic parameters like the direct-to-reverberation ratio (DRR) are useful tools but still perform weakly as shown in the acoustic characterization of environments (ACE) challenge. In this paper, we develop a novel dual-channel metric based on the modulation energy doma…
Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a learnable function or is not encapsulated into the deep learning optimization. Consequently, most of the existing methods rely on a post processing step using the…
Reverberation and noise are known to be the two most important culprits for poor performance in far-field speech applications, such as automatic speech recognition. Recent research has suggested that reverberation-aware speech enhancement (or speech technologies, in general) could be used to improve…