Speech is a fundamental language capability of humans, and in artificial intelligence (AI) and natural language processing, Text-to-Speech (TTS) or speech synthesis plays a crucial role. It enables machines to generate coherent and natural speech from written text, finding wide-ranging applications in human communication and undergoing extensive research in AI, natural language processing, and speech processing.
Effective TTS requires expertise in languages, speech production, linguistics, acoustics, digital signal processing, and machine learning. Recent advancements in deep learning have led to significant improvements in neural network-based TTS, enhancing the quality of synthesized speech and making it more human-like.
The Evolution of Speech Synthesis
The quest to develop machines capable of synthesizing human speech traces its roots back to the 12th century. In the 18th century, Hungarian scientist Wolfgang von Kempelen constructed a speaking machine that used mechanical components to produce basic words and short sentences.
Significant advancements in computer-based speech synthesis occurred in the latter half of the 20th century, with early methods including articulatory, formant, and concatenative synthesis. Articulatory synthesis aimed to simulate human speech production through articulator movements but faced practical challenges due to data collection and modeling difficulties.
Formant synthesis employed linguistically derived rules to control a simplified source-filter model, resulting in intelligible speech with moderate computational resources. However, the synthesized speech lacked naturalness and had artifacts, posing challenges in formulating accurate synthesis rules.
Concatenative synthesis utilized databases of pre-recorded speech units concatenated to generate speech-matching input text. While intelligible, this approach required extensive databases and struggled with reproducing natural prosody and emotions.
To overcome concatenative synthesis limitations, statistical parametric speech synthesis (SPSS) was introduced, generating acoustic parameters and using algorithms to produce speech waveforms. It consisted of text analysis, parameter prediction (acoustic model), and vocoder analysis and synthesis (vocoder). SPSS offered improved naturalness, flexibility, and data efficiency but faced challenges in speech intelligibility and the robotic quality of the generated voice.
Deep Learning for Speech Synthesis
With the rise of deep learning, neural network-based TTS (neural TTS) emerged, using neural networks as the model backbone for speech synthesis. Early neural models replaced hidden Markov models (HMMs) in SPSS for acoustic modeling. WaveNet was a pioneering modern neural TTS model that generated waveforms directly from linguistic features.
Other models, such as DeepVoice 1/2, upgraded SPSS components with neural network-based models. End-to-end TTS models, such as Char2Wav, Tacotron 1/2, Deep Voice 3, and FastSpeech 1/2, simplified text analysis and directly used character and phoneme sequences as input, enhancing voice quality and reducing human preprocessing needs. Fully end-to-end TTS systems like ClariNet, FastSpeech 2s, EATS, and NaturalSpeech also achieve waveform generation directly from text, offering improved intelligibility and naturalness without complex feature development.
Understanding AI-Powered Speech Synthesis
In neural TTS, categorization is based on fundamental TTS components: text analysis, acoustic models, vocoders, and fully end-to-end models, aligning with the data flow from text to waveform generation. The process involves various data representations, including characters, linguistic features, acoustic features, and waveforms.
In statistical parametric speech synthesis (SPSS), acoustic features such as line spectral pairs (LSP), mel-cepstral coefficients (MCC), mel-generalized coefficients (MGC), and band aperiodicities (BAP) are used and converted into waveforms through vocoder models like STRAIGHT and WORLD. In neural-based end-to-end TTS, mel- or linear-spectrograms are acoustic features transformed into waveforms using neural-based vocoders.
Text analysis converts input text into linguistic features essential for pronunciation and prosody context. In SPSS, tasks such as text normalization, word segmentation, part-of-speech tagging, and grapheme-to-phoneme conversion are performed. End-to-end models use character or phoneme sequences as direct input, simplifying text analysis.
Acoustic models generate acoustic features from linguistic features, phonemes, or characters. SPSS uses HMMs, deep neural networks (DNNs), or recurrent neural networks (RNNs) to predict features such as MCC, MGC, and BAP. End-to-end models employ encoder-attention-decoder structures or feed-forward networks to generate linear spectrograms or waveforms.
Vocoders convert acoustic features into waveforms. Early neural vocoders such as WaveNet and WaveRNN use linguistic features directly, while advancements use mel-spectrograms for faster processing. Autoregressive, flow-based, generative adversarial network (GAN)-based, and diffusion-based vocoders improve voice quality in TTS systems.
Fully end-to-end (E2E) TTS models directly generate waveforms from characters or phonemes, offering advantages like reduced human annotation and lower costs. Notable models such as WaveNet, Tacotron, FastSpeech, and ClariNet contribute to this progress, enhancing efficiency and accuracy in TTS.
Recent research shows that an E2E approach, directly modeling the raw waveform from text, produces more natural speech than traditional neural text-to-speech (TTS) systems. However, current E2E models are computationally demanding. To address this, Alex AI researchers introduced the Lightweight E2E-TTS (LE2E) model, which generates high-quality speech with minimal computational resources.
Real-World Applications of Speech Synthesis
Synthetic speech finds wide-ranging applications in various domains, offering numerous benefits and advancements. Communication aids have evolved from basic talking calculators to sophisticated 3D applications like talking heads tailored to specific needs. The field of synthetic speech is rapidly expanding, with TTS systems continuously improving in quality and becoming more affordable for everyday use, empowering individuals with communication difficulties.
An important use of speech synthesis lies in assisting the visually impaired with reading and communication tools. TTS technology has made reading machines more accessible and customizable, providing natural speech for the visually impaired.
Similarly, synthetic speech benefits deaf and vocally handicapped individuals, enabling them to communicate with non-sign language speakers. Adjustable voice characteristics and additional tools facilitate expressing emotions and enhancing communication speed.
Educational applications also benefit from synthesized speech, aiding dyslexic individuals in learning to read and write. Speech synthesizers integrated with word processors assist in proofreading and error detection. Educators employ TTS systems to create captivating digital learning modules, enhancing students' cognitive abilities and retention levels.
In marketing and advertising, synthetic speech helps establish distinct brand images and offers cost savings by eliminating the need for traditional voice actors. Speech generation tools open fascinating possibilities for crafting engaging audio and video content, from YouTube videos to audiobooks and podcasts.
With an ever-widening application field, synthetic speech has become an integral part of human-machine interactions, from warning systems to desktop messages.
Challenges and The Path Forward
Speech synthesis involves creating artificial speech using technologies such as audio deepfakes, AI-generated audio or edited audio that mimics real speech. Detecting audio deepfakes is crucial due to their involvement in criminal activities. Deepfake audio generation methods outpace prevention and detection, but strategies include blockchain for data provenance and emotional cues to combat deepfakes.
Detection involves using features fed into the network and various DNNs like ResNet, with the challenge of “generalization” requiring improved networks, features, and diverse loss functions. Researchers are encouraged to explore unique distinguishing characteristics beyond spectral audio signal features such as mel-frequency cepstral coefficients (MFCCs), speaker vocal traits, or neuron activation patterns. Simplifying pre-processing for obtaining distinguishing characteristics should also be a focus.
TTS systems aim to achieve high-quality speech synthesis that sounds natural, expressive, and intelligible. Powerful generative models, such as variational autoencoders, GANs, Flow, or Diffusion, can enhance speech synthesis efficiency and quality. Improving TTS generalizability to diverse domains in the future is crucial for robustness. Modeling natural conversational styles enhances human-like speech, and data-efficient TTS techniques using unsupervised or semi-supervised learning and cross-lingual transfer learning reduce synthesis costs.
Moreover, voice adaptation for target speakers with limited data is valuable for data efficiency, while parameter-efficient TTS is essential for resource-constrained devices. Advancing TTS requires exploring powerful models, improving representation learning, and enabling expressive synthesis while optimizing efficiency. Creating more human-like speech and optimizing data, parameters, and energy efficiency will help shape the future of TTS technology, making it accessible across various applications.
References and Further Readings
Ning, Yishuang, Sheng He, Zhiyong Wu, Chunxiao Xing, and Liang-Jie Zhang. (2019). A Review of Deep Learning Based Speech Synthesis. Applied Sciences 9, no. 19: 4050. DOI: https://doi.org/10.3390/app9194050
Xu Tan. (2023). Neural Text-To-Speech Synthesis. Artificial Intelligence: Foundations, Theory and Algorithms, Springer. DOI: https://doi.org/10.1007/978-981-99-0827-1
Tan, X., Qin, T., Soong, F., and Liu, T.-Y. (2021). A Survey on Neural Speech Synthesis. arXiv. https://arxiv.org/pdf/2106.15561.pdf
Tura Vecino, B. et al. (2023). Lightweight End-to-end Text-to-speech Synthesis for low resource on-device applications, 12th Speech Synthesis Workshop (SSW), 2023.
Khanjani Z, Watson G and Janeja VP. (2023). Audio deepfakes: A survey. Frontiers Big Data 5:1001063. DOI: https://doi.org/10.3389/fdata.2022.1001063