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Text-to-Speech Synthesis: an Overview
Le 08/08/2021
Making a computer recite a fairy tale was one of the funniest things I ever did with a computer when I was a kid. You may copy a sentence into a window and hear a colourless metallic voice struggle through commas before stopping weaving a strangely accented storey. It was a miracle at the time.
The purpose of TTS ( converter Text to Speech technology) nowadays isn't just to make robots talk, but to make them sound like people of various ages and genders. In terms of perspective, we won't be able to tell the difference between listening to machine-voiced audiobooks and news on TV or communicating with virtual assistants.
What are the key competitors in the field and how can it be accomplished?
Measurements of quality
Text to Voice system synthesisers are typically judged on a variety of variables, including intelligibility, naturalness, and preference of speech synthesis, as well as human perception factors like comprehensibility.
Intelligibility: refers to the quality of the audio produced, or the degree to which each word in a sentence is produced.
Naturalness: refers to the quality of the speech in terms of temporal structure, pronunciation, and emotion portrayal.
Preference: listeners' preference for a better TTS; preference and naturalness are determined by TTS system, signal quality, and voice, both separately and together.
Comprehensibility: the degree to which received messages are comprehended.
Approaches of TTS Conversion
Computer science and artificial intelligence advances have influenced text to speech voice systems that have evolved throughout time in response to recent trends and new possibilities in data collection and processing.
While concatenative TTS and parametric TTS have long been the two main methods of Text-to-Speech conversion, the Deep Learning revolution has brought a new perspective to the problem of speech synthesis, shifting the focus away from human-developed speech features and toward fully machine-obtained parameters.
The purpose of TTS ( converter Text to Speech technology) nowadays isn't just to make robots talk, but to make them sound like people of various ages and genders. In terms of perspective, we won't be able to tell the difference between listening to machine-voiced audiobooks and news on TV or communicating with virtual assistants.
What are the key competitors in the field and how can it be accomplished?
Measurements of quality
Text to Voice system synthesisers are typically judged on a variety of variables, including intelligibility, naturalness, and preference of speech synthesis, as well as human perception factors like comprehensibility.
Intelligibility: refers to the quality of the audio produced, or the degree to which each word in a sentence is produced.
Naturalness: refers to the quality of the speech in terms of temporal structure, pronunciation, and emotion portrayal.
Preference: listeners' preference for a better TTS; preference and naturalness are determined by TTS system, signal quality, and voice, both separately and together.
Comprehensibility: the degree to which received messages are comprehended.
Approaches of TTS Conversion
Computer science and artificial intelligence advances have influenced text to speech voice systems that have evolved throughout time in response to recent trends and new possibilities in data collection and processing.
While concatenative TTS and parametric TTS have long been the two main methods of Text-to-Speech conversion, the Deep Learning revolution has brought a new perspective to the problem of speech synthesis, shifting the focus away from human-developed speech features and toward fully machine-obtained parameters.
- Concatenative TTS
- Formant Synthesis
- Parametric
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