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In the next section, we introduce how a HiFiNet vocoder is trained during the creation of a neural voice model. With each vocoder update, the speech generated sounds clearer, voice less muffled and noises reduced. “ Top cinematographers weigh in on filmmaking in the age of streaming.” Let’s hear the difference of the audio quality with samples generated using different neural vocoders based on the same acoustic features (recommended to listen with a high-quality headset).Ģ020 vocoder for real-time synthesis (HiFiNet) In specific, it directly impacts the fidelity of a wave, including clearness, timbre, etc. The vocoder is critical to the final audio quality. Finally, the Neural Vocoder converts the acoustic features into audible waves so the synthetic speech is generated. Then the phoneme sequence goes into the Neural Acoustic Model to predict acoustic features, which defines speech signals, such as speaking style, speed, intonations, and stress patterns, etc. Sequence of phonemes defines the pronunciations of the words provided in the text. A phoneme is a basic unit of sound that distinguishes one word from another in a particular language. To generate natural synthetic speech from text, first, text is input into Text Analyzer, which provides output in the form of phoneme sequence. Microsoft Azure Neural TTS consists of three major components in the engine: Text Analyzer, Neural Acoustic Model, and Neural Vocoder. Neural vocoder is a specific vocoder design which uses deep learning networks and is a critical module of Neural TTS. It turns an intermediate form of the audio, which is called acoustic feature, into audible waveform. Vocoder is a major component in speech synthesis, or text-to-speech. What is a vocoder and why does it matter? All these benefits are achieved through a new-generation neural vocoder, called HiFiNet. In addition, it can synthesize speech much faster than our previous version of the product. Our tests show that this new vocoder generates audios without hearable quality loss from the recordings of training data (more details are introduced later). The voice fidelity has been improved significantly and audio quality defects such as glitches and small noises are largely reduced.
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Our recent updates on Azure Neural TTS voices include a major upgrading of the vocoder. This is particularly beneficial to customers whose scenario relies on hi-fi audios or long interactions, including video dubbing, audio books, or online education materials. Today we are glad to share that we have upgraded our Neural TTS voices with a new-generation vocoder, called HiFiNet, which results much higher audio fidelity while significantly improving the synthesis speed. Voice quality, which includes the accuracy of pronunciation, the naturalness of prosody such as intonation and stress patterns, and the fidelity of audio, is the key reason that customers are migrating from the traditional TTS voices to neural voices. Since its launch, we have seen it widely adopted in a variety of scenarios by many Azure customers, from voice assistants like the customer service bot like BBC and Poste Italiane, to audio content creation scenarios like Duolingo.
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Neural Text to Speech (Neural TTS), a powerful speech synthesis capability of Cognitive Services on Azure, enables you to convert text to lifelike speech which is close to human-parity. This post was co-authored with Jinzhu Li and Sheng Zhao