Sunday 02 February 2025
The quest for a more efficient and accurate way to generate speech synthesis has been ongoing in the field of artificial intelligence. Recently, researchers have made significant progress in developing self-supervised learning models that can extract useful information from raw audio data without requiring labeled transcriptions.
One such approach is called Generative Spoken Language Modeling (GSLM), which uses a neural network to predict hidden units not directly observed in the input audio data. These hidden units are then used as discrete symbols, which are converted into synthesized speech through a process called unit2speech.
In a new study, researchers explored the effectiveness of GSLM in generating high-quality speech synthesis without relying on traditional text transcriptions. They created three different models for synthesizing speech using input representations from text labels, automatic speech recognition (ASR) models, and self-supervised learning models.
The results showed that while the ASR model performed better than the self-supervised learning model in terms of linguistic aspects such as word error rate and phoneme error rate, the self-supervised learning model outperformed the ASR model in terms of naturalness and acoustic quality. The study also found that increasing the length of the discrete symbols obtained through k-means clustering improved the performance of the speech synthesis system.
Interestingly, the researchers observed that the language dependency of the self-supervised learning model had a significant impact on the quality of the synthesized speech. When the model was trained on data from a specific language, it performed better than when it was trained on data from multiple languages.
The study also highlighted the importance of considering acoustic quality metrics such as WARP-Q and SDR in evaluating speech synthesis systems. These metrics provide valuable insights into the degree of distortion and noise present in the synthesized speech.
Overall, this research demonstrates the potential of self-supervised learning models for generating high-quality speech synthesis without relying on traditional text transcriptions. The findings have significant implications for the development of more efficient and effective speech synthesis systems that can be used in a variety of applications, from voice assistants to language translation software.
Cite this article: “Self-Supervised Learning Models for High-Quality Speech Synthesis”, The Science Archive, 2025.
Speech Synthesis, Self-Supervised Learning, Generative Spoken Language Modeling, Neural Network, Hidden Units, Unit2Speech, Automatic Speech Recognition, Asr, Naturalness, Acoustic Quality
