In this section, you will be acquainted with the engines and languages that are supported by Unifonic Text-to-Speech Services.
Standard text-to-speech (TTS) and neural text-to-speech represent different generations of technology used to convert text into spoken language. The key difference between standard TTS and neural TTS lies in the underlying technology and the quality of speech they produce. Neural TTS, thanks to deep learning techniques and large datasets, offers more natural and expressive speech synthesis, making it the preferred choice for many modern applications.
|Technology and Methods||Standard TTS systems typically use rule-based or concatenative methods to generate speech. In rule-based systems, linguistic rules and phonetic dictionaries are used to synthesize speech. Concatenative systems piece together prerecorded segments of human speech to form words and sentences. These methods have limitations in naturalness and expressiveness.||Neural TTS, on the other hand, relies on deep learning techniques, such as deep neural networks (DNNs) and recurrent neural networks (RNNs), or more advanced models like WaveNet or Tacotron. These models are capable of generating more natural and human-like speech by learning patterns from large datasets of recorded speech.|
|Naturalness and Expressiveness||Standard TTS can produce robotic or monotone speech that may lack naturalness and expressiveness. It may struggle with the intonation, prosody, and nuances present in human speech.||Neural TTS models have the potential to produce highly natural and expressive speech. They can mimic human speech patterns, including variations in pitch, tone, and emphasis, resulting in more human-like intonation and emotion.|
|Training Data||Standard TTS systems often rely on smaller datasets and require substantial manual engineering for linguistic and phonetic rules.||Neural TTS models benefit from large and diverse datasets, enabling them to generalize better across languages and accents. These models do not require extensive manual rule creation.|
|Flexibility and Adaptability||Standard TTS systems are generally less flexible and adaptable. Making changes to the voice or style of speech may require significant manual effort.||Neural TTS models are more flexible and adaptable. They can be fine-tuned to generate speech in different voices, styles, or accents with relative ease.|
|Quality and Realism||Standard TTS can produce speech that may sound robotic or artificial, which can be a limitation in applications requiring high-quality, natural-sounding speech.||Neural TTS models offer a higher level of quality and realism, making them suitable for various applications like virtual assistants, audiobooks, and more.|
|Languages Supported by Unifonic||English, Arabic, Dutch, Filipino, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Mandarin, Portuguese, Russian, Spanish, Turkish, Vietnamese||English, Arabic, Urdu, Hindi*|
New Language Support in Standard TTS engine
Dutch, Filipino, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Mandarin, Portuguese, Russian, Spanish, Turkish, Vietnamese
Updated 17 days ago