What is the methodology of speech synthesis?

Mon, Dec 2023
Bard AI

Speech synthesis, also called text-to-speech (TTS), is the artificial production of human speech. It involves converting text input into a waveform or audio file, which can be played back on a computer or other device. Speech synthesis is used in a variety of applications, such as:

  • Virtual assistants, such as Siri and Alexa, use speech synthesis to provide spoken responses to user queries.
  • Educational software, such as reading tutors, use speech synthesis to read aloud text passages to students.
  • Media players, such as VLC and QuickTime, use speech synthesis to provide audio descriptions of videos for visually impaired users.
  • Telephone systems, such as automated answering services, use speech synthesis to provide recorded messages to callers.

Speech synthesis is a complex process that involves a number of steps. The first step is to convert the text input into a phonetic representation. This can be done using a variety of methods, such as:

  • Rule-based methods, which use a set of rules to map text characters to phonemes.
  • Statistical methods, which use statistical models to learn the mapping between text and phonemes.
  • Neural network methods, which use neural networks to learn the mapping between text and phonemes.

Once the text input has been converted into a phonetic representation, the next step is to generate a waveform or audio file. This can be done using a variety of methods, such as:

  • Concatenative synthesis, which involves concatenating together pre-recorded phonemes.
  • Recurrent neural network (RNN) synthesis, which involves using a RNN to generate the waveform directly from the phonetic representation.
  • WaveNet synthesis, which involves using a WaveNet, a type of generative adversarial network (GAN), to generate the waveform directly from the phonetic representation.

The final step in the speech synthesis process is to apply a variety of post-processing effects to the generated waveform. This can include:

  • Noise reduction, to remove unwanted noise from the waveform.
  • Equalization, to adjust the frequency balance of the waveform.
  • Compression, to reduce the dynamic range of the waveform.

Speech synthesis is a rapidly developing field, and new techniques are constantly being developed to improve the quality of the generated speech. As a result, speech synthesis is becoming increasingly more widely used in a variety of applications.

ChatGPT 4
Speech synthesis, also known as text-to-speech (TTS), is the artificial production of human speech. It involves the conversion of written text into spoken words. The methodology of speech synthesis can be broken down into several steps: 1. Text Analysis: This is the first step in the process where the system analyzes and understands the text that needs to be converted into speech. It involves processes like tokenization (breaking down text into words, phrases, symbols, or other meaningful elements), text normalization (converting abbreviations, numbers, and symbols to words), and part-of-speech tagging. 2. Phonetic Analysis: After understanding the text, it is then converted into phonetic transcription. This process involves assigning phonetic transcriptions to each word and dividing the text into prosodic units like phrases, clauses, or sentences. 3. Prosody Generation: Prosody refers to the rhythm, stress, and intonation of speech. In this step, appropriate prosodic features are assigned to each unit based on linguistic rules. 4. Speech Synthesis: Finally, using a speech synthesizer (which could be either formant-based or concatenative-based), these phonetic transcriptions are converted into sound. There are two main types of methodologies used in TTS systems: 1. Concatenative TTS: This method uses large databases of recorded speech which are cut up and reassembled to create complete utterances. The quality of output depends on the size and quality of the database. 2. Parametric TTS: In this method, a mathematical model is used to generate speech based on parameters such as pitch and duration extracted from an analysis of human speech. In recent years, deep learning techniques have been applied to improve both concatenative and parametric methods for more natural sounding synthesized speech.
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