after looking at its efficiency and diversity purchased it in the year 2013. It should be noted that the Mean Opinion Score for a real person’s recording was 4.7. It collected the highest Mean Opinion Score of 3.9 beating all its competitors. IVONA Text to Speech is such a powerful application that it has received laurels at an international exhibition in the city of Bonn, Germany. The application has been developed by IVONA software based in Poland. IVONA Text to Speech is a multilingual application which will read out loud and clearly whatever you will type. It is full offline installer standalone setup of IVONA Text to Speech Engine for 32/64bit PC. IVONA Text to Speech Free Download Latest version for Windows. "Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram". "WaveGlow: A Flow-based Generative Network for Speech Synthesis". "Parallel WaveNet: Fast High-Fidelity Speech Synthesis". "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis". "Almost Unsupervised Text to Speech and Automatic Speech Recognition". "Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis". "Semi-Supervised Generative Modeling for Controllable Speech Synthesis". "Hierarchical Generative Modeling for Controllable Speech Synthesis". "High-fidelity speech synthesis with WaveNet". This issue has been solved by Parallel WaveGAN, which learns to produce speech through multi-resolution spectral loss and GAN learning strategies. However, despite the high inference speed, parallel WaveNet has the limitation of needing a pre-trained WaveNet model, so that WaveGlow takes many weeks to converge with limited computing devices. Meanwhile, Nvidia proposed a flow-based WaveGlow model, which can also generate speech faster than real-time. Since such inverse autoregressive flow-based models are non-auto-regressive when performing inference, the inference speed is faster than real-time. Parallel WaveNet is an inverse autoregressive flow-based model which is trained by knowledge distillation with a pre-trained teacher WaveNet model. To solve this problem, Parallel WaveNet was proposed.
However, the auto-regressive nature of WaveNet makes the inference process dramatically slow. Given an input text or some sequence of linguistic unit Y is conditioned on the samples at all previous timesteps. Some DNN-based speech synthesizers are approaching the naturalness of the human voice. The deep neural networks are trained using a large amount of recorded speech and, in the case ofĪ text-to-speech system, the associated labels and/or input text.
Deep learning speech synthesis uses Deep Neural Networks (DNN) to produceĪrtificial speech from text (text-to-speech) or spectrum (vocoder).
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