Advancements in Speech-to-Singing Technology

Converting speech to singing is a challenging task in the field of technology. This process often struggles because it needs speech and singing data that match perfectly. There are two big problems in this area: not enough matching data and difficulties in making sure the content matches the right pitch. These challenges lead to poor results. To address these issues, a new method called SVPT was introduced. This method uses self-supervised training to help improve the process.

SVPT takes advantage of techniques from speech recognition to help with rhythm matching and learn things without needing to see data beforehand. It uses random changes to the data and alters pitch, allowing the method to work with unpaired singing data, which helps solve the problem of not having enough data. SVPT also has applications in singing voice synthesis, which can scale up the models used for this purpose.

#Background

The speech-to-singing conversion system takes spoken words and transforms them into singing. This process needs to maintain the meaning of the words while changing the way they sound. This work not only enhances music entertainment but also helps to connect advanced speech models with the more basic models used for singing.

Even though there have been improvements in this area, problems still arise. The lack of paired speech and singing data remains a major issue. Most existing methods rely on datasets that are smaller than the amount of singing data available. Additionally, the previous models struggled with aligning the speech content properly.

The new approach to tackling these challenges involves breaking the modeling process into two stages. Instead of working directly with the sound, the models will first map the prompts into a simpler version that still holds the meaning. This method has been successful in speech generation but has not translated well to singing voice synthesis due to the complex nature of singing.

#Self-Supervised Learning

Self-supervised learning is a method where a model learns from data that does not have labels. In this context, the models can improve without needing specific text annotations. This is beneficial for singing voice conversion, as it helps deal with the misalignment of data. The second stage of the model helps turn general meanings into actual sound, which eliminates the need for detailed transcripts.

This method can effectively handle the rhythm and pitch components of singing, allowing it to learn from unannotated data. This means researchers can train the models using large amounts of available data that may not be fully labeled, which is a significant advantage.

#The Proposed Method: SVPT

SVPT stands for Self-Supervised Singing Voice Pre-Training. It is a new approach to converting speech into singing and improving synthesis for singing voices. This method uses a type of model called a Transformer, which is useful for working with long sequences of data.

#Model Structure

The model consists of two main parts: a global model that looks at the whole input and a local model that focuses on smaller sections. This setup allows it to manage long pieces of audio. The input is broken down into smaller parts, which makes it easier for the model to process. Each part's features are combined to improve understanding.

#Training Process

The training uses singing data without any annotations. This is done by combining semantic tokens (which carry meaning) with pitch information to create the outputs. The model is trained to generate sound outputs from the input without needing to know the specific details of each sound ahead of time.

This approach only requires basic information about pitch and connects it with segments of audio, allowing for a more efficient learning process.

#Challenges in Singing Voice Data

Singing voice data has unique features that pose challenges for training models. Unlike speech data, which tends to follow specific patterns, singing is much more variable. This means that using standard methods for speech modeling doesn't always work well for singing.

#Data Scarcity

One primary issue is that there is not enough paired speech and singing data available for training. The existing datasets often do not include enough samples to create effective models, thus limiting performance.

#Rhythm and Pitch Variation

The difference in rhythm and pitch between speech and singing adds another layer of complexity. The rhythm in singing can change significantly compared to speech, making direct modeling difficult.

To tackle these issues, the method introduces several strategies to prepare the data for better training outcomes.

#Information Perturbation Techniques

This method implements changes to the data to help prevent overfitting and improve the model's performance. By altering both the pitch and rhythm information, they can create a more stable training set.

#Pitch and Timbre Changes

To ensure that the model focuses on meaning rather than specific sounds, pitch and timbre features are intentionally changed. This helps to detach the identity of the speaker from the singing sound, allowing the model to learn the content without bias.

#Rhythm Adjustments

Changing the rhythm is also a crucial step. The model uses random sampling to alter the rhythm of singing voice data. This strategy helps to mix up the patterns while still holding onto the essential information.

#Model Implementation

The practical application of the model is straightforward but requires substantial computation resources. The model takes unlabelled singing data and uses it to create a training routine. The training process is resource-intensive but makes use of the available data to optimize learning.

#Multi-Scale Transformer

The model uses a multi-scale Transformer structure. This type of model can process long audio inputs effectively by breaking them into manageable parts. The different layers focus on different aspects of the audio, enhancing the learning process.

#Training Setup

During training, the model uses a large dataset comprising singing and speech data. This extensive training helps the model learn to generate outputs that match the desired singing characteristics while still retaining the meaning of the input speech.

#Results

The experimental results show that SVPT significantly improves both the speech-to-singing conversion process and the singing voice synthesis tasks. This approach has been tested against various benchmarks, demonstrating its effectiveness across different types of data.

#Objective Evaluation

The performance has been measured using established practices to compare the quality of the generated audio outputs. Objectives such as log-spectral distance have been implemented to measure how well the model reconstructed the desired sound quality.

#Subjective Evaluation

Listeners were asked to assess quality, naturalness, and the overall likeness to the original singing. This subjective assessment provides additional insights into the model's quality and effectiveness, confirming the successful outcomes of the study.

#Comparison with Other Methods

SVPT was compared with existing technologies in the field. The results indicate that SVPT outperformed other models in various metrics. Its ability to learn from unannotated data gives it a considerable edge over traditional methods that require extensive labeled datasets.

#Future Directions

Moving forward, there are still challenges to address. The model relies heavily on pitch information, and further research is needed to ensure its applicability in practical situations. Additionally, since the method uses substantial computational power, that aspect should be optimized.

#Conclusion

The introduction of SVPT marks a significant advancement in the field of speech-to-singing conversion. By utilizing self-supervised learning and innovative data perturbation strategies, the method shows promise in enhancing the quality and efficiency of the conversion process.

In conclusion, the methods discussed here highlight the possibilities for future developments in technology that can connect speech and singing more effectively. By moving forward with these innovations, researchers can continue to improve the capabilities of singing voice synthesis and speech-to-singing conversion.