How do you incorporate emotion, style, and personality into speech synthesis models and outputs?

1 Challenges of speech synthesis

Speech synthesis is not a simple task. It involves many aspects of linguistics, acoustics, and signal processing, as well as the challenges of dealing with different languages, dialects, and accents. Moreover, speech synthesis needs to capture not only the content of the text, but also the context, the intention, and the emotion of the speaker. For example, the same sentence can be spoken in different ways depending on the mood, the tone, the situation, and the relationship of the speaker and the listener. How can speech synthesis models learn to generate such variations and nuances?

2 Methods of speech synthesis

There are different methods of speech synthesis, each with its own advantages and disadvantages. The most common ones are concatenative, parametric, and neural. Concatenative synthesis uses recorded speech segments from a human speaker and concatenates them to form new utterances. It produces high-quality speech, but it requires a large database of recordings and it is limited by the availability and diversity of the voice samples. Parametric synthesis uses mathematical models to generate speech waveforms from acoustic features. It is more flexible and scalable, but it often sounds synthetic and unnatural. Neural synthesis uses deep neural networks to learn the mapping between text and speech from data. It can produce natural and expressive speech, but it requires a lot of computational resources and data.

3 Advances in speech synthesis

In recent years, there have been numerous developments in speech synthesis, particularly in neural synthesis. End-to-end models, for instance WaveNet, WaveRNN and WaveGlow, generate speech waveforms from text without intermediate steps or features, making the pipeline simpler and reducing errors and artifacts. Multi-speaker models, like Tacotron 2, Deep Voice 3 and VoiceLoop, are able to generate speech from different speakers by conditioning on speaker identities or learning from speaker embeddings. Style and emotion models, such as GST-Tacotron, Emo-TTS and ESDA, can generate speech with a variety of styles and emotions by either conditioning on style or emotion labels or learning from prosodic features. Prosody models like Prosody-Tacotron, FastSpeech and HiFi-GAN are able to generate speech with the correct prosody such as pitch, intonation, stress and rhythm to improve the intelligibility and fluency of the speech.

4 Resources for speech synthesis

If you're looking to gain more knowledge or experience with speech synthesis, there are plenty of resources available online. Papers with Code provides a comprehensive list of papers and code on speech synthesis, as well as benchmarks and leaderboards on various tasks and datasets. Mozilla TTS is an open-source project that offers a toolkit for building state-of-the-art neural speech synthesis models in multiple languages and with multiple speakers. Google Cloud Text-to-Speech and Amazon Polly are cloud-based services that offer high-quality speech synthesis using WaveNet and multi-speaker models, respectively, with over 220 voices and 40 languages. Lastly, CoVoST is a large-scale multilingual speech translation dataset that can be used for speech synthesis, recognition, and translation; it covers 21 languages and over 40 accents.