Advances in Text-to-Speech: A Comprehensive Overview of Speech Synthesis Models and Techniques

Speech synthesis, also known as Text to Speech (TTS), is a fascinating area of research that involves generating speech from text. Over the years, there have been several approaches to speech synthesis, with concatenation synthesis and statistical parametric synthesis being the most prominent ones.

Concatenation synthesis involves the concatenation of pre-recorded speech segments, such as full sentences, words, or individual phones. These segments are stored in the form of waveforms or spectrograms, and at runtime, the desired sequence is created by selecting the best chain of candidate units from the database.

On the other hand, statistical parametric synthesis aims to extract a set of parameters during training that characterize the audio sample, such as the frequency spectrum, fundamental frequency, and duration of speech. These parameters are then used to synthesize the final speech waveforms at runtime.

Speech synthesis models are evaluated using Mean Opinion Score (MOS), which measures the quality of the generated speech based on human ratings. Today, benchmarks for speech synthesis are performed on various datasets in different languages to assess the quality of the models.

With the advancement of deep learning, there has been a significant improvement in speech synthesis models. Models like WaveNet, Tacotron, and Deep Voice have pushed the boundaries of TTS by achieving impressive results in terms of both quality and efficiency.

WaveNet, for example, was the first model to successfully model the raw waveform of the audio signal using autoregressive modeling. It achieved high-quality speech synthesis results at a fast pace. Other models like Tacotron and Deep Voice introduced innovative architectures that improved the overall performance of TTS systems.

FastSpeech, Parallel WaveNet, and EATS are some of the recent advancements in speech synthesis that have focused on speeding up the inference process and enhancing the quality of generated speech.

Overall, text to speech technology has evolved significantly over the years, thanks to advancements in deep learning and neural networks. As researchers continue to explore new techniques and architectures, we can expect further improvements in the field of speech synthesis, making it more accessible and efficient for a wide range of applications.