Author name cluster

Luis C. Cobo

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5 papers

2 author rows

ICLR Conference 2020 Conference Paper

High Fidelity Speech Synthesis with Adversarial Networks

Mikolaj Binkowski
Jeff Donahue
Sander Dieleman
Aidan Clark
Erich Elsen
Norman Casagrande
Luis C. Cobo
Karen Simonyan

Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (Fréchet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS. We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator. Listen to GAN-TTS reading this abstract at https://storage.googleapis.com/deepmind-media/research/abstract.wav

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ICML Conference 2018 Conference Paper

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Aäron van den Oord
Yazhe Li
Igor Babuschkin
Karen Simonyan
Oriol Vinyals
Koray Kavukcuoglu
George van den Driessche 0002
Edward Lockhart

The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today’s massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, a 1000x speed up relative to the original WaveNet, and capable of serving multiple English and Japanese voices in a production setting.

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AIJ Journal 2014 Journal Article

Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains

Luis C. Cobo
Kaushik Subramanian
Charles L. Isbell
Aaron D. Lanterman
Andrea L. Thomaz

Reinforcement learning (RL) and learning from demonstration (LfD) are two popular families of algorithms for learning policies for sequential decision problems, but they are often ineffective in high-dimensional domains unless provided with either a great deal of problem-specific domain information or a carefully crafted representation of the state and dynamics of the world. We introduce new approaches inspired by these two techniques, which we broadly call abstraction from demonstration. Our first algorithm, state abstraction from demonstration (AfD), uses a small set of human demonstrations of the task the agent must learn to determine a state-space abstraction. Our second algorithm, abstraction and decomposition from demonstration (ADA), is additionally able to determine a task decomposition from the demonstrations. These abstractions allow RL to scale up to higher-complexity domains, and offer much better performance than LfD with orders of magnitude fewer demonstrations. Using a set of videogame-like domains, we demonstrate that using abstraction from demonstration can obtain up to exponential speed-ups in table-based representations, and polynomial speed-ups when compared with function approximation-based RL algorithms such as fitted Q-learning and LSPI.

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AAMAS Conference 2012 Conference Paper

Automatic Task Decomposition and State Abstraction from Demonstration

Luis C. Cobo
Charles L. Isbell Jr.
Andrea Thomaz

Both Learning from Demonstration (LfD) and Reinforcement Learning (RL) are popular approaches for building decision-making agents. LfD applies supervised learning to a set of human demonstrations to infer and imitate the human policy, while RL uses only a reward signal and exploration to find an optimal policy. For complex tasks both of these techniques may be ineffective. LfD may require many more demonstrations than it is feasible to obtain, and RL can take an inadmissible amount of time to converge. We present Automatic Decomposition and Abstraction from demonstration (ADA), an algorithm that uses mutual information measures over a set of human demonstrations to decompose a sequential decision process into several subtasks, finding state abstractions for each one of these subtasks. ADA then projects the human demonstrations into the abstracted state space to build a policy. This policy can later be improved using RL algorithms to surpass the performance of the human teacher. We find empirically that ADA can find satisficing policies for problems that are too complex to be solved with traditional LfD and RL algorithms. In particular, we show that we can use mutual information across state features to leverage human demonstrations to reduce the effects of the curse of dimensionality by finding subtasks and abstractions in sequential decision processes.

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IJCAI Conference 2011 Conference Paper

Automatic State Abstraction from Demonstration

Luis C. Cobo
Peng Zang
Charles L. Isbell Jr.
Andrea L. Thomaz

Learning from Demonstration (LfD) is a popular technique for building decision-making agents from human help. Traditional LfD methods use demonstrations as training examples for supervised learning, but complex tasks can require more examples than is practical to obtain. We present Abstraction from Demonstration (AfD), a novel form of LfD that uses demonstrations to infer state abstractions and reinforcement learning (RL) methods in those abstract state spaces to build a policy. Empirical results show that AfD is greater than an order of magnitude more sample efficient than jus tusing demonstrations as training examples, and exponentially faster than RL alone.

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