TTS Technologies

Existing TTS Technologies:

LSTM Networks (Long Short-Term Memory Networks):

These are a type of recurrent neural network (RNN) used in earlier TTS systems. They are good at handling sequences, making them suitable for speech generation where context and continuity are important.

While effective, LSTM-based systems are gradually being surpassed by newer neural network architectures in terms of naturalness and efficiency.


Tacotron is an end-to-end generative text-to-speech model developed by Google. It simplifies the speech synthesis process by directly converting text to audio without intermediate steps.

Tacotron and its successor, Tacotron 2, have been highly influential, but they are part of an ongoing evolution in speech synthesis technology.


Wavenet was also developed by Google, WaveNet is a deep neural network for generating raw audio waveforms. It produces more natural and human-like speech compared to older techniques.

WaveNet set a new standard for natural-sounding speech and has been integrated into various Google products, including Google Assistant.

Transformer-Based Models:

Following the success of transformers in natural language processing (like GPT models), there's a growing interest in applying transformer architectures to TTS. These models could offer major improvements in speech quality and efficiency.

Diffusion Models:

Inspired by the success of diffusion models in image generation (like DALL-E, Stable Diffusion, MidJourney, Leonardo-AI etc.), researchers are exploring Diffusion Model’s application in audio synthesis, including TTS. These models could potentially generate even more natural and diverse speech patterns.

Personalization and Voice Cloning:

Advances in AI are making it easier to customize speech synthesis to specific voices or styles, and even clone voices with minimal data. This raises both exciting possibilities and of course ethical considerations – like the problems we are already encountering with ‘Deep Fakes’. The cloned voices are so real that it is hard to distinguish real voices from fake, even by experts in the field.

Cross-Modal Learning:

These are Systems that learn from both text and audio data (and, even video) to generate speech that's more expressive and context-aware, matching speech patterns to emotional cues or visual contexts.

Energy and Resource Efficiency:

With the increasing use of AI models in various devices, there's a growing emphasis on making these models more energy-efficient and capable of running on devices with limited computing power – such as mobile phones and common household appliances. One example is the concept of an LLM in the role of a ‘Supervisor’ or ‘Manager’ invoking various AI models to empower a device when needed. Just imagine your refrigerator carrying on a conversation with you in your grandmother’s real voice or your automobile navigation device giving instructions in your dad’s voice. That would be really cool !.

Text-to-Speech technologies competing with Tacotron and WaveNet:

Deep Voice (by Baidu):

This is a deep learning text-to-speech model from Baidu that matches WaveNet audio quality using a fraction of the training data.

VoiceLoop (by Meta):

Announced in 2022, this model from Meta efficiently learns speech patterns directly from raw audio and text without a large dataset.

Coqui (by Skerry Lab):

An open-source toolkit for building neural voice models using convolutional networks rather than recurrent networks as in Tacotron.

FastSpeech (by Microsoft):

From Microsoft Research Asia, this non-autoregressive model improves training speed and stability over previous autoregressive models.

Eats (by Apple):

Though details are limited, Apple utilizes an enhanced version of Tacotron called Ernst in production across Siri voices.

Some key advantages these newer models have include better efficiency, smaller footprint, and more robustness compared to the groundbreaking but slower Tacotron and WaveNet architectures initially introduced by Google. Competition remains extremely fierce in high-quality real-time TTS for applications.

The above are some major trends in Speech Synthesis and TTS. It is important to note that while newer models like transformers and diffusion models and other creative multi-modal approaches are emerging, existing technologies like Tacotron and WaveNet continue to be highly relevant and are often used as foundations for further innovations in the field. The future of TTS is likely to see a blend of these technologies, continuously improving the quality, naturalness, and versatility of synthetic speech.

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