Advancements in Multi-Speaker Text-to-Speech Technology

Text-to-speech (TTS) technology has made significant progress over the years, largely due to advancements in deep learning and the availability of extensive training data. TTS systems can convert written text into spoken words, enabling various applications, including virtual assistants and accessibility tools.

#What is Multi-Speaker TTS?

Multi-speaker TTS refers to systems capable of mimicking the voices of different speakers. Traditionally, TTS systems required a lot of recordings from each specific speaker to create a speaking model. However, new techniques allow for creating voices by using only small amounts of data from each speaker.

#Zero-Shot and Few-Shot TTS Approaches

In this context, there are two important approaches-zero-shot and few-shot TTS.

• Zero-Shot TTs: This method allows the system to generate speech for a new speaker it has never encountered before, without needing any additional recordings of that speaker.

• Few-Shot TTS: This method requires only a small number of recordings (from seconds to a few minutes) of the target speaker's voice for the system to learn how to replicate that speaker's voice.

These approaches make it much easier to produce synthetic voices for various applications, especially for those who cannot provide a lot of voice data.

#The Role of the SpeechT5 Model

The SpeechT5 model is a new multi-speaker TTS model. It has been trained on a large amount of speech and text data, making it capable of generating diverse and high-quality voices. The model was designed to work well in both the zero-shot and few-shot scenarios.

During our research, we tested this model’s performance using recordings from well-known Czech politicians and celebrities. This allowed evaluators to compare the synthetic voices to real voices confidently.

#Data Collection and Preprocessing

To develop the SpeechT5 model effectively, we gathered a vast amount of Czech speech and text data. The speech data was collected from various sources, including television broadcasts, radio programs, podcasts, and more. This collection amounted to over 120,000 hours of speech, making it a unique resource for developing TTS technology in Czech.

For text data, we utilized a large web archive that contains crawled web pages. We filtered this data to retain only clean and relevant text. After cleaning, we ended up with 530 million web pages worth of text, allowing for comprehensive training of the SpeechT5 model.

#Training the SpeechT5 Model

We conducted the training of the SpeechT5 model by utilizing a high-performance setup with multiple Graphics Processing Units (GPUs). The training involved multiple steps, where the model learned to predict missing data from the speech and text it was provided. This self-supervised learning phase allowed the model to gain a foundational understanding of speech and text.

After the initial pre-training, we focused on fine-tuning the model specifically for the multi-speaker TTS task. This required a clean and diverse dataset that included different voices and speaking styles.

#Finishing the Fine-Tuning Process

The fine-tuning involved using various datasets, including those with professionally recorded speech and other sources to enhance diversity. We ensured the collected data was of high quality, discarding noisy or poorly transcribed examples.

Once fine-tuning was completed, our model had the capability to replicate voices based on just a few samples of a speaker's voice. This helped us achieve the desired quality in synthetic speech for a variety of voices.

#Testing the Model's Capabilities

After completing the training, we conducted tests to evaluate how well the model performed. We selected a diverse group of speakers, including politicians and celebrities, to ensure a good mix of voices.

To evaluate the quality and similarity of the synthetic voices to real voices, we organized listening tests. Participants listened to both synthetic and genuine recordings and provided feedback on how similar they sounded.

#Results from the Listening Tests

The results showed that the SpeechT5 model performed poorly in zero-shot scenarios, meaning that it struggled to generate high-quality speech for speakers it had never encountered before. In contrast, the few-shot models performed significantly better after being fine-tuned with just a minute of speech data from a target speaker.

The fine-tuning improved both the quality and similarity of the generated speech, especially for voices that were more expressive. Adding more training data beyond one minute did not show substantial benefits in quality but did enhance similarity for more dynamic voices.

#Applications and Implications

The ability to create synthetic voices with minimal samples opens up many possibilities. For instance, it can help preserve the voices of individuals who have lost their ability to speak due to medical conditions. Additionally, it offers a way to generate voices for new productions without legal issues over voice rights.

Moreover, the technology can enrich various applications, including chatbots, virtual assistants, and storytelling platforms by providing diverse voices.

#Ethical Considerations

While this technology holds great potential, it also poses risks, especially regarding misuse. The ability to create voices of famous individuals could lead to disinformation or unauthorized use in media. As a result, it is crucial to approach the release of such technology with caution and implement safeguards against misuse.

#Conclusion

In summary, the SpeechT5 model represents a significant advancement in multi-speaker TTS technology. By enabling both zero-shot and few-shot capabilities, it allows for the generation of synthetic voices with minimal data. Our results indicate that while the zero-shot performance needs improvement, the few-shot approach shows great promise.

The research opens new avenues for creating realistic synthetic voices for various applications while raising important ethical questions that need to be addressed as the technology continues to evolve.