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Patrick Lumban Tobing

5 accepted papers

2024

Mapache: Masked Parallel Transformer for Advanced Speech Editing and Synthesis

ICASSP 2024accepted

Recent advancements in Generative AI, such as scaled Transformer large language models (LLM) and diffusion decoders, have revolutionized speech synthesis. With speech encompassing the complexities of natural language and audio dimensionality, many recent models have relied on autoregressive modeling…

Cited by 0SourceScholar
2022

Direct Noisy Speech Modeling for Noisy-To-Noisy Voice Conversion

ICASSP 2022accepted

Beyond the conventional voice conversion (VC) where the speaker information is converted without altering the linguistic content, the background sounds are informative and need to be retained in some real-world scenarios, such as VC in movie/video and VC in music where the voice is entangled with ba…

Cited by 0SourceScholar
2021

Crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder

ICASSP 2021accepted

In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank. Although we have released an open-source VC software based on the Gaussian mixture model named sprocket in the last VC Challenge, it is not straightforward to apply any speech cor…

Cited by 0SourceScholar
2020

Efficient Shallow Wavenet Vocoder Using Multiple Samples Output Based on Laplacian Distribution and Linear Prediction

ICASSP 2020accepted

This paper presents a novel way for an efficient implementation scheme of shallow WaveNet vocoder with multiple samples (segment) output based on the use of Laplacian distribution and linear prediction. In our previous work, we have proposed a shallow architecture for WaveNet vocoder that utilizes o…

Cited by 0SourceScholar
2019

Voice Conversion with Cyclic Recurrent Neural Network and Fine-tuned Wavenet Vocoder

ICASSP 2019accepted

This paper presents a novel framework for providing high-quality parallel voice conversion (VC) using a cyclic recurrent neural network (RNN) and a finely tuned WaveNet vocoder. Using the proposed system, we are tackling the quality degradation issue faced by WaveNet when it is fed with estimated (o…

Cited by 0SourceScholar