Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

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 http://tiny.cc/gantts.
The Text-to-Speech (TTS) task consists in the conversion of text into speech audio.
In recent years, the TTS field has seen remarkable progress, sparked by the development of neural autoregressive models for raw audio waveforms such as WaveNet (van den Oord et al., 2016) , SampleRNN (Mehri et al., 2017) and WaveRNN (Kalchbrenner et al., 2018) .
A notable limitation of these models is that they are difficult to parallelise over time: they predict each time step of an audio signal in sequence, which is computationally expensive and often impractical.
A lot of recent research on neural models for TTS has focused on improving parallelism by predicting multiple time steps in parallel, e.g. using flow-based models (van den Oord et al., 2018; Ping et al., 2019; Prenger et al., 2019; Kim et al., 2019) .
Such highly parallelisable models are more suitable to run efficiently on modern hardware.
An alternative approach for parallel waveform generation would be to use Generative Adversarial Networks (GANs, Goodfellow et al., 2014) .
GANs currently constitute one of the dominant paradigms for generative modelling of images, and they are able to produce high-fidelity samples that are almost indistinguishable from real data.
However, their application to audio generation tasks has seen relatively limited success so far.
In this paper, we explore raw waveform generation with GANs, and demonstrate that adversarially trained feed-forward generators are indeed able to synthesise high-fidelity speech audio.
Our contributions are as follows:
• We introduce GAN-TTS, a Generative Adversarial Network for text-conditional highfidelity speech synthesis.
Its feed-forward generator is a convolutional neural network, coupled with an ensemble of multiple discriminators which evaluate the generated (and real) audio based on multi-frequency random windows.
Notably, some discriminators take the linguistic conditioning into account (so they can measure how well the generated audio corresponds to the input utterance), while others ignore the conditioning, and can only assess the general realism of the audio.
• We propose a family of quantitative metrics for speech generation based on Fréchet Inception Distance (FID, Heusel et al., 2017) and Kernel Inception Distance (KID, Bińkowski et al., 2018) , where we replace the Inception image recognition network with the DeepSpeech audio recognition network.
• We present quantitative and subjective evaluation of TTS-GAN and its ablations, demonstrating the importance of our architectural choices.
Our best-performing model achieves a MOS of 4.2, which is comparable to the state-of-the-art WaveNet MOS of 4.4, and establishes GANs as a viable option for efficient TTS.
2 RELATED WORK
Random window discriminators.
Although it is difficult to say why RWDs work much better than the full discriminator, we conjecture that this is because of the relative simplicity of the distributions that the former must discriminate between, and the number of different samples we can draw from these distributions.
For example, the largest window discriminators used in our best model discriminate between distributions supported on R 3600 , and there are respectively 371 and 44,401 different windows that can be sub-sampled from a 2s clip (real or generated) by conditional and unconditional RWDs of effective window size 3600.
The full discriminator, on the other hand, always sees full real or generated examples sampled from a distribution supported on R 48000 .
Computational efficiency.
Our Generator has a larger receptive field (590ms, i.e. 118 steps at the frequency of the linguistic features) and three times fewer FLOPs (0.64 MFLOP/sample) than Parallel WaveNet (receptive field size: 320ms, 1.97 MFLOP/sample).
However, the discriminators used in our ensemble compare windows of shorter sizes, from 10ms to 150ms.
Since these windows are much shorter than the entire generated clips, training with ensembles of such RWDs is faster than with FullD.
In terms of depth, our generator has 30 layers, which is a half of Parallel WaveNet's, while the depths of the discriminators vary between 11 and 17 layers, as discussed in Appendix A.2.
Stability.
The proposed model enjoyed very stable training, with gradual improvement of subjective sample quality and decreasing values of the proposed metrics.
Despite training for as many as 1 million steps, we have not experienced model collapses often reported in GAN literature and studied in detail by Brock et al. (2019) .
We have introduced GAN-TTS, a GAN for raw audio text-to-speech generation.
Unlike state-ofthe-art text-to-speech models, GAN-TTS is adversarially trained and the resulting generator is a feed-forward convolutional network.
This allows for very efficient audio generation, which is important in practical applications.
Our architectural exploration lead to the development of a model with an ensemble of unconditional and conditional Random Window Discriminators operating at different window sizes, which respectively assess the realism of the generated speech and its correspondence with the input text.
We showed in an ablation study that each of these components is instrumental to achieving good performance.
We have also proposed a family of quantitative metrics for generative models of speech: (conditional) Fréchet DeepSpeech Distance and (conditional) Kernel DeepSpeech Distance, and demonstrated experimentally that these metrics rank models in line with Mean Opinion Scores obtained through human evaluation.
As they are based on the publicly available DeepSpeech recognition model, they will be made available for the machine learning community.
Our quantitative results as well as subjective evaluation of the generated samples showcase the feasibility of text-to-speech generation with GANs.
A ARCHITECTURE DETAILS
We introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech, which achieves Mean Opinion Score (MOS) 4.2.