What are some advanced techniques in speech-synthesis that can be used to improve the quality and naturalness of generated speech?
Incorporating linguistic and phonetic knowledge into the synthesis process can also result in more accurate and natural-sounding speech.
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Another technique is prosody modeling, which focuses on capturing the intonation, rhythm, and emphasis of human speech to make synthesized speech sound more lifelike.
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One advanced technique is neural network-based vocoders, such as WaveNet or Tacotron, which can generate speech that sounds remarkably natural and expressive.
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Lastly, leveraging techniques from natural language processing and text-to-speech alignment can help generate speech that better reflects the intended meaning and context of the text being synthesized.
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Some researchers are also exploring the use of generative adversarial networks (GANs) to improve speech synthesis by training the models to distinguish between real and synthesized speech.
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Using deep learning models, such as recurrent neural networks or convolutional neural networks, can also greatly enhance the quality of speech synthesis by capturing complex patterns in audio data.
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