Text-to-speech, short: TTS, is a technology that converts written text into spoken language.
It is also known as speech synthesis or voice synthesis. The process involves taking text input and using artificial intelligence algorithms to generate natural-sounding speech in a human-like voice.
30-45 Minutes to read
Background
Creating a synthetic voice for speech output, text-to-speech (TTS) synthesis, involves using machine learning and speech synthesis techniques to generate human-like speech from text.
Here are the general steps to create a synthetic voice
Data Collection
Gather a substantial amount of high-quality audio data from a human speaker. This dataset should include the speaker reading various texts in various styles and tones.
Data Preprocessing
Clean and preprocess the audio data to remove noise, background sounds, and other artifacts. Segment the audio into smaller units, such as phonemes, words, or sentences, and align them with the corresponding text.
Feature Extraction
Extract relevant features from the audio data, such as acoustic features, prosody (intonation and rhythm), and phonetic information. Common features include Mel-Frequency Cepstral Coefficients (MFCCs), pitch, and duration.
Building a TTS Model
Choose a suitable TTS architecture, such as concatenative synthesis, HMM-based synthesis, or neural TTS models. Train the TTS model using the preprocessed audio and text data
Neural TTS models like WaveNet, Tacotron, and Transformer-based models have gained popularity for their natural-sounding speech.
Text Processing
Convert the input text into a phonemic or phonetic representation to make it compatible with the TTS model. This step is crucial for models that rely on phonetic information.
Synthesis
Use the trained TTS model to generate speech from text inputs. The model will predict the corresponding speech waveform or spectrogram.
Waveform Generation
Convert the predicted spectrogram or other representations into a waveform using a vocoder. Popular vocoders include Griffin-Lim, WORLD, or WaveNet-based vocoders like WaveGlow or Tacotron 2’s Griffin-Lim.
Post-processing
Apply post-processing techniques to the generated waveform to improve quality. Post-processing may include smoothing, pitch adjustment, or filtering.
Voice Customization (optional)
Fine-tune the synthetic voice to make it sound more like a specific character or individual if desired.
Integration
Integrate the synthetic voice into your application or platform, providing text inputs and receiving synthesized speech as output.
Testing and Evaluation
Conduct thorough testing and evaluation to ensure the synthetic voice meets your quality and performance requirements. Collect user feedback and make improvements as needed.
Continuous Improvement
Continue to collect and label data for retraining the TTS model to improve the quality of the synthetic voice over time. It’s important to note that creating a high-quality synthetic voice requires substantial resources, including access to a large amount of data, computational power, and expertise in machine learning and signal processing.
Additionally, several pre-trained TTS models and APIs are available that can simplify the process, making it more accessible for developers and businesses to implement synthetic voices in their applications without building everything from scratch.