We live in a world where computer technology is becoming an inseparable part of our lives. From self-driving cars to chatbots, everyday routines are being transformed to become more efficient and simplified. Speech synthesis is one of the spheres which has revolutionized our interaction with devices, from Apple’s “Siri” to Amazon’s “Alexa”, we can manage our alarms, music playlists, and even make remote calls without pressing a button.

From the robotic and barely decipherable voices of early voice synthesizers, to human-like answering machines, powered by artificial intelligence, speech synthesis has undergone a long history of experimentation that came with trial and success. In this article, we are going to dive deeper into the origins of early voice synthesis, the operating processes of deep neural networks, and discuss how modern speech synthesis is taking over the digital world.

The beginning of speech synthesis

In 1846, a strange yet compelling contraption was presented to the London public. Joseph Faber, a German-born scientist, created Euphonia, “a machine that boasted the ability to replicate human speech.” The Euphonia resembled a piano with attached bellows and a realistic-looking head of a woman.

It was created after over 25 years of research and can be considered a complex engineering marvel. Fourteen piano keys controlled its speech, while a bellows and reed simulated breathing. Faber even managed to program the device to sing a version of “God Save the Queen”.

The first attempt to electronically synthesize human speech happened in the late 1930s. Homer Dudley, an employee of the Bell Telephone Company, had invented Voder (Voice Operating Demonstrator). It used a combination of manual controls to simulate human speech, including pitch, buzz/hiss, and formant frequencies. However, it was only used for research purposes without the implication of massive production. Fortunately, there is actual footage of Voder in use:

It wasn’t until the 1980s that text-to-speech (TTS) technology became more relevant in computer science. Denis Klatt, an MIT professor, was at the forefront of creating usable synthesizers, such as MITalk and DECtalk, but the general public was mostly acquainted with Perfect Paul, notably used by the late scientist Stephen Hawking.

As we can see, the evolution of speech synthesis has experienced a few twists and turns and a handful of experiments to reach the polished version that we encounter nowadays.

The mechanism of speech synthesis

To understand how speech synthesis works, it’s essential to break it down into its basic components. The focus of TTS involves two key processes:

• Text Processing (Natural Language Processing – NLP): Natural Language Processing (NLP) enables the system to analyze and interpret written language by breaking it down into its linguistic components. This includes tokenization (identifying words and punctuation), parsing (analyzing grammatical structure), and applying phonetic transcription to understand how words should be pronounced. NLP also helps in recognizing the correct intonation, understanding context, and disambiguating meaning based on syntax and semantics. By incorporating these processes, the system can generate speech that is contextually appropriate and natural-sounding, mimicking human-like pronunciation and prosody.

• Speech Generation (Phonetic Rendering): Once the system has processed the text, it then generates speech by converting phonetic data into waveforms. These waveforms are either pre-recorded human speech segments stitched together (concatenative synthesis) or generated from scratch using algorithms that model human vocal tract behavior (parametric synthesis).

Modern speech synthesis is an intricate process, yet with the fast-paced development of neural networks, the quality of synthetic speech has improved dramatically in recent years.

The Breakthrough of Neural Networks

As we have mentioned, neural networks have accelerated the development of speech synthesis. Deep learning models, such as recurrent neural networks (RNNs) and generative adversarial networks (GANs), have been shown to produce highly realistic and expressive speech.

• RNNs: RNNs are particularly well-suited for modeling sequential data like speech. They can capture the long-term dependencies in speech, leading to more natural-sounding output.

• GANs: GANs consist of a generator network that produces speech samples and a discriminator network that evaluates their quality.

The usage of these two networks can generate highly realistic and diverse speech.

Systems like Google’s WaveNet and OpenAI’s GPT-4 use neural networks to generate speech that is almost indistinguishable from a human voice.

Neural networks, especially deep learning models and large language models can learn complex patterns in speech data, allowing them to generate more natural-sounding speech that can then be used to provide TTS options for various applications, such as audiobooks or the aforementioned “Alexa”.

Most importantly, these models can be trained on large datasets of human speech to mimic specific voices or accents. As Natural Language Processing continues to evolve, we can expect even more elaborate and realistic AI-powered speech generation in the near future.

Applications of speech synthesis

The breakthrough of speech synthesis can be useful for various fields, from improving accessibility to helping with daily errands.

Accessibility

Speech synthesis can make the information accessible to individuals with visual impairments. Text-to-speech (TTS) enables people to access written content through audio, whether it’s audiobooks, lectures, or exhibit descriptions in a museum.

Education and learning

Language learning tools use speech synthesis to provide accurate pronunciation guides and practice exercises, helping learners improve their language skills. Additionally, speech synthesis can be integrated into educational games and applications, making learning more engaging and effective for children.

Entertainment and Media

Speech synthesis has had a great impact on the entertainment and media industry. Virtual assistants, such as Siri, Alexa, and Google Assistant, rely on speech synthesis to provide clear and human-like responses to user queries. In the realm of video games and animation, speech synthesis enhances storytelling by providing realistic voice-overs for characters.

Communication and information

Customer service chatbots use speech synthesis to interact with customers in a polite and engaging manner, providing an effective user experience. Interactive voice response systems (IVRS) rely on speech synthesis to guide callers through menus and provide information, making it easier to navigate automated phone services. Thus, speech synthesis can be used for automatic responses in banks, hospitals, or other relevant industries. In addition, speech synthesis can also generate automated news and weather updates, delivering timely information through directional speakers and other devices.

Final remarks

The evolution of speech synthesis from early experimental devices to intricate neural network models has transformed our relationship with technology. From education to entertainment, speech synthesis has been applied to various fields and disciplines. As AI continues to grow and envelope our daily lives, we can expect even more impressive and natural-sounding voices to emerge, further enriching our digital experiences.