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Advancements in Neural Network-Based TTS (2000s)
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== Impact == The evolution of neural network models have ushered in transformative impacts across various facets of speech synthesis, notably enhancing the quality, expressiveness, and versatility of synthesized speech. ==== Enhanced Quality of Speech Synthesis ==== Neural network-based vocoders, such as WaveNet, have significantly improved the quality of synthesized speech, providing more natural and expressive voice outputs. This has been pivotal in reducing the robotic tones often occurs in earlier TTS systems. Using neural network techniques like Tacotron 2 and WaveNet, we can process transcript-free noisy speech datasets in more precise way. Based on it we can create a model capable of generating audio in speakers' voices that not present in the original data. ==== Prosody Modeling ==== The emergence of neural network models in speech synthesis has dramatically influenced prosody modeling, which involves the prediction and generation of prosodic features like pitch, duration, and energy. It is crucial for producing speech that sounds rhythmic and sentimental. It enables the synthesis of expressive and emotional speech by learning and generating varied prosody, which is crucial for conveying different emotions and speaking styles. Neural network based end-to-end text to speech can facilitate the development of controllable speech synthesis systems where prosody can be manipulated to generate speech with desired pitch, stress, and rhythm.<ref name=":1" /> ==== End-to-End Systems ==== Neural network-based vocoders on End-to-End (E2E) Systems can directly convert text to speech without requiring intermediate representations. It enables systems to learn complex mappings from input to output and often simplifying the traditional multi-stage processing pipeline. Tacotron, an end-to-end generative text-to-speech model can achieve speech synthesis directly from characters, which significantly reduces requirements of expertise of acoustic and many other domain.<ref name=":4" />This has also enabled the creation of speech that can be more dynamically adjusted to various contexts and emotional tones, enhancing applications like virtual assistants and conversational agents. ==== Generative Modeling ==== Neural networks have enabled the development of generative models that can produce high-quality, natural-sounding speech, improving upon traditional concatenative and parametric methods. It can generate speech with varied emotional content, and can be trained to mimic different speakers, accents, and styles, providing versatility in speech synthesis applications. Generative models have advantages in situations with limited training data to synthesize speech, enabling the creation of voices for speakers with limited available recordings.<ref name=":4" />
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