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. Our recent updates on Azure Neural TTS voices include a major upgrading of the vocoder. The voice fidelity has been improved significantly and audio quality defects such as glitches and small noises are largely reduced. Our tests show that this new vocoder generates audios without hearable quality loss from the recordings of training data (more details are introduced later). In addition, it can synthesize speech much faster than our previous version of the product. What is a vocoder and why does it matter? All these benefits are achieved through a new-generation neural vocoder, called HiFiNet. Vocoder is a major component in speech synthesis, or text-to-speech. It turns an intermediate form of the audio, which is called acoustic feature, into audible waveform. Neural vocoder is a specific vocoder design which uses deep learning networks and is a critical module of Neural TTS. Microsoft Azure Neural TTS consists of three major components in the engine: Text Analyzer, Neural Acoustic Model, and Neural Vocoder. To generate natural synthetic speech from text, first, text is input into Text Analyzer, which provides output in the form of phoneme sequence. A phoneme is a basic unit of sound that distinguishes one word from another in a particular language. Sequence of phonemes defines the pronunciations of the words provided in the text. 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. Finally, the Neural Vocoder converts the acoustic features into audible waves so the synthetic speech is generated.
The vocoder is critical to the final audio quality.
“ 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. With each vocoder update, the speech generated sounds clearer, voice less muffled and noises reduced. In the next section, we introduce how a HiFiNet vocoder is trained during the creation of a neural voice model.
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