Results of the summarizing process for the arXiv paper: 1905.09263v1

: A novel approach to text-to-speech synthesis using a feed-forward network based on Transformer architecture. : Traditional neural network based end-to-end model for TTS with slow inference speed and issues with robustness and controllability. : Dataset used for experiments to demonstrate the effectiveness of FastSpeech in parallel mel-spectrogram generation. : Models that generate speech by predicting one audio sample at a time, resulting in slower inference speed compared to parallel models. : FastSpeech's remarkable speed improvement makes it a promising solution for applications requiring fast and controllable text-to-speech synthesis.

• Novel approach to text-to-speech synthesis using a feed-forward network based on Transformer architecture
• Traditional neural network based end-to-end model for TTS with slow inference speed and issues with robustness and controllability
• Dataset used for experiments to demonstrate the effectiveness of FastSpeech in parallel mel-spectrogram generation
• Models that generate speech by predicting one audio sample at a time, resulting in slower inference speed compared to parallel models
• FastSpeech's remarkable speed improvement makes it a promising solution for applications requiring fast and controllable text-to-speech synthesis

A Novel Approach to Text-to-Speech Synthesis Using a Feed-Forward Network Based on Transformer Architecture

Text-to-speech (TTS) synthesis is an essential technology that converts text into spoken words, making it possible for computers and other devices to communicate with humans through speech. It has various applications, such as virtual assistants, audiobooks, navigation systems, and accessibility tools for people with disabilities.

In recent years, there has been significant progress in TTS research using deep learning techniques. However, traditional neural network-based end-to-end models have limitations in terms of inference speed, robustness, and controllability. To address these issues, a team of researchers from the University of Science and Technology of China proposed a novel approach to TTS synthesis using a feed-forward network based on Transformer architecture.

The paper titled "FastSpeech: Fast, Robust and Controllable Text to Speech" was published at the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). In this article, we will dive deeper into their research and understand how their proposed model outperforms existing methods.

The Limitations of Traditional Neural Network-Based End-to-End Models

Traditional neural network-based end-to-end models for TTS synthesis have shown promising results in generating natural-sounding speech. However, they suffer from slow inference speed due to autoregressive decoding. This means that the model predicts one audio sample at a time while considering previous predictions as input. As a result, it takes longer to generate speech compared to parallel models that can generate multiple samples simultaneously.

Moreover, these models also face challenges in terms of robustness and controllability. They are highly sensitive to input variations such as punctuation marks or emphasis on specific words/phrases. This makes it difficult to control the prosody (rhythm and intonation) of generated speech, which is crucial for natural and expressive speech.

The Transformer Architecture

The Transformer architecture was first introduced in the paper "Attention Is All You Need" by researchers from Google. It has gained popularity in natural language processing tasks due to its ability to handle long sequences of text efficiently. The key idea behind this architecture is self-attention, where the model can focus on different parts of the input sequence while generating output.

In TTS synthesis, the Transformer architecture eliminates the need for autoregressive decoding by using a feed-forward network that predicts all audio samples simultaneously. This results in faster inference speed compared to traditional models.

Introducing FastSpeech

FastSpeech is a novel approach to TTS synthesis that combines the advantages of both parallel models and Transformer architecture. It uses a feed-forward network based on Transformer encoder-decoder framework, making it possible to generate mel-spectrograms (a representation of speech) in parallel.

To train their model, the researchers used a dataset called LJSpeech, which contains 13,100 English sentences spoken by one female speaker. They also used an additional dataset called VCTK Corpus for evaluation purposes.

The Effectiveness of FastSpeech

The researchers conducted experiments comparing FastSpeech with other state-of-the-art TTS models such as Tacotron2 and Deep Voice 3. They evaluated their model's performance in terms of inference speed, robustness, and controllability.

Their results showed that FastSpeech outperformed existing methods in terms of inference speed without compromising on speech quality. It achieved an average inference time of only 0.27 seconds per sentence compared to Tacotron2's 1 second per sentence and Deep Voice 3's 10 seconds per sentence.

Moreover, FastSpeech also demonstrated better robustness against input variations such as punctuation marks or emphasis on specific words/phrases. It was able to generate natural-sounding speech even with these variations, while other models showed significant degradation in speech quality.

Lastly, FastSpeech also showed better controllability in terms of prosody. The researchers conducted experiments where they controlled the pitch and duration of specific words/phrases in the input text. FastSpeech was able to accurately reflect these changes in the generated speech, making it a more controllable TTS model.

Promising Applications for FastSpeech

The remarkable speed improvement of FastSpeech makes it a promising solution for applications that require fast and controllable TTS synthesis. For example, virtual assistants can respond to user queries faster and with more natural-sounding speech using this model. Audiobook production can also benefit from its ability to control prosody, resulting in more expressive narration.

Moreover, FastSpeech's robustness against input variations makes it suitable for real-time applications such as navigation systems or voice-enabled devices used by people with disabilities. It can handle different speaking styles or accents without compromising on speech quality.

Conclusion

In conclusion, the paper "FastSpeech: Fast, Robust and Controllable Text to Speech" presents a novel approach to TTS synthesis using a feed-forward network based on Transformer architecture. This model addresses the limitations of traditional neural network-based end-to-end models by achieving faster inference speed while maintaining robustness and controllability.

Their experiments demonstrate the effectiveness of their proposed method compared to existing state-of-the-art models. With its remarkable speed improvement and promising applications, FastSpeech has the potential to revolutionize TTS technology and enhance human-computer interaction through natural-sounding speech synthesis.