Advancements in Neural Network-Based TTS (2000s)

Introduction

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Historical Context

The history of neural network-based text-to-speech (TTS) can be traced back to the early days of artificial intelligence research. In the 1980s, researchers began to explore the use of neural networks to model the human speech production process. In the 1990s, Hidden Markov Models were introduced to TTS, which brought significant improvements in speech synthesis. HMM-based systems allowed for better control of speech characteristics and were widely adopted for several years.

In early 2000s, researchers started exploring the use of deep neural networks (DNNs) for speech synthesis. However, it wasn’t until the introduction of generative adversarial networks (GANs) and autoregressive models that the quality of synthesized speech improved significantly. In recent years, the development of deep learning and artificial intelligence has led to a surge in research on neural network-based TTS.

One of the key breakthroughs in neural network-based TTS came in 2006 with the introduction of the WaveNet model by Google AI. WaveNet was the first neural network-based TTS system to generate high-quality speech waveforms directly from text, without the need for intermediate representations such as phonemes or mel spectrograms. This led to a significant improvement in the naturalness and expressiveness of synthesized speech.

Key Innovations

Deep Neural Network-Based Text-to-Speech (TTS) has undergone a remarkable transformation whose evolution marks a shift from traditional rule-based and statistical methods towards neural network-driven solutions. Researchers have continually improved the quality and efficiency of TTS systems during this period. In the 2000s, DNN-based TTS models began to gain prominence, paving the way for more natural and expressive speech synthesis. Commencing with the introduction of foundational models, we shall elucidate the subsequent advancements in Text-to-Speech (TTS) technology. In this paragrah, some working mechanism are introduced to readers.

DNN-Based Acoustic Modeling and Vocoder

TTS-Modeling

Tacotron: Tacotron is an end-to-end generative Text-to-Speech (TTS) model that directly synthesizes speech from input characters, utilizing a sequence-to-sequence (seq2seq) architecture with attention. It avoids the need for phoneme-level alignment.

Innovation:

• End-to-End Generative Model: Tacotron is an end-to-end generative model that synthesizes speech directly from characters, eliminating the need for intermediate linguistic features or acoustic models.
• Sequence-to-Sequence Model with Attention: It is based on a sequence-to-sequence model with attention, enabling high-accuracy and natural speech generation.
• No Phoneme-Level Alignment Required: Tacotron doesn't necessitate phoneme-level alignment, simplifying scalability to large data with transcripts.
• Faster Frame-Level Generation: Tacotron generates speech at the frame level, making it considerably faster than sample-level autoregressive methods like WaveNet.
• High Subjective Mean Opinion Score (MOS): Tacotron attains a 3.82 out of 5 on the subjective mean opinion score, surpassing a production parametric system in terms of naturalness, particularly for US English.

Tacotron 2: Tacotron 2 is a neural network architecture for direct text-to-speech synthesis. It consists of two core elements: a feature prediction network and a modified WaveNet vocoder.

Innovation:

• Compact Acoustic Intermediate Representation: Tacotron 2 utilizes mel spectrograms, providing a streamlined representation of speech features and reducing WaveNet's architectural complexity.
• Modified WaveNet Vocoder: Tacotron 2 adapts WaveNet architecture to convert mel spectrograms into time-domain waveform samples, achieving audio quality akin to human speech.
• Integration of Tacotron and WaveNet: Combining Tacotron-style prosody modeling and WaveNet vocoder, Tacotron 2 delivers state-of-the-art sound quality in a unified, neural approach to speech synthesis.

FastSpeech: FastSpeech is a neural text-to-speech (TTS) system that addresses the challenges of slow inference speed, lack of robustness, and lack of controllability in traditional TTS models. It uses a feed-forward network based on Transformer to generate mel-spectrograms in parallel, allowing for faster synthesis.

Innovation:

• Multi-head attention mechanism: Enhances long-range dependency modeling and parallelization by allowing simultaneous attention to different parts of the input sequence.
• Positional encoding: Provides positional information to input sequence elements, aiding in distinguishing elements with identical values.
• Layer normalization: Improves training stability by normalizing inputs to each network layer.
• Stacked self-attention layers: Enables the network to learn multiple representation levels of the input sequence, enhancing output quality.
• No recurrence or convolution: Unlike traditional architectures, the Transformer network omits recurrent connections and convolutions, resulting in improved efficiency and parallelizability.

Transformer: The Transformer is a novel neural network architecture introduced by Vaswani et al. in 2017. It stands out for its exclusive reliance on attention mechanisms, foregoing recurrent connections and convolutions. In natural language processing, particularly neural machine translation, it has achieved remarkable success. The Transformer comprises an encoder and a decoder, both structured with stacks of identity blocks. It employs multi-head self-attention to model dependencies between input and output sequences.

Features:

• Multi-Head Attention Efficiency: In Transformer, multi-head attention in Tacotron2 enhances training efficiency, constructing hidden states in the encoder and decoder concurrently, speeding up training by 4.25 times and effectively addressing long-range dependencies.
• WaveNet Vocoder's Role: Within the Transformer TTS network, WaveNet vocoder synthesizes high-quality audio from mel spectrograms, closely resembling human recordings on specific datasets.
• Transformer Architecture: The Transformer, introduced by Vaswani et al. in 2017, is a unique neural network architecture solely based on attention mechanisms. It excels in natural language processing tasks like neural machine translation, comprising encoder and decoder stacks of identity blocks and utilizing multi-head self-attention to model input-output dependencies.

Vocoder

WaveNet: WaveNet serves as a vital component in Tacotron 2 for speech synthesis. It transforms mel-scale spectrograms, predicted by the feature prediction network, into time-domain waveform samples, resulting in high-quality audio waveforms.In this architecture, WaveNet is adapted to function as a vocoder. It takes the predicted mel spectrograms and uses dilated convolutional layers organized into dilation cycles.

These layers' dilation rates determine their receptive field sizes, allowing WaveNet to capture long-term dependencies across frames.

In Tacotron 2, WaveNet is conditioned on the mel spectrograms generated by the feature prediction network. This conditioning simplifies the speech synthesis process and produces high-quality audio output.

Parallel WaveGAN: Parallel WaveGAN is a waveform generator utilizing GANs. It employs non-autoregressive WaveNet with multi-resolution spectrogram and adversarial loss functions for high-quality speech waveform synthesis. Unlike traditional models, it doesn't require complex density distillation, offering faster, efficient, small-footprint, and competitive waveform generation suitable for real-time applications.

Articulatory Features-Based TTS（加时间，并提前）

Articulatory feature-based Text-to-Speech (TTS) is a concept that involves using articulatory features, which represent the movements and positions of the speech articulators (such as the tongue, lips, and jaw), as the basis for synthesizing speech. This approach aims to capture the detailed articulatory information present in the speech signal, allowing for more natural and expressive speech synthesis.

Method:

• Data Collection: High-quality speech data is collected, often using techniques such as electromagnetic articulography (EMA) or magnetic resonance imaging (MRI), to capture the articulatory movements during speech production .
• Articulatory Feature Extraction: The collected data is processed to extract articulatory features, which represent the relevant articulatory movements and positions. This can be done using algorithms such as the ACIDA algorithm, which combines statistical analysis and geometric modeling to extract articulatory features from the data .
• Articulatory Feature Mapping: The extracted articulatory features are then mapped to the corresponding linguistic units, such as phonemes or graphemes, in the text. This mapping can be done using statistical models or machine learning techniques .
• Synthesis: The mapped articulatory features are used to drive a speech synthesis system, which generates the corresponding speech waveform. This can be achieved using techniques such as concatenative synthesis, where pre-recorded speech units are concatenated based on the articulatory features, or parametric synthesis, where the articulatory features are used to control a speech synthesis model.

Algorithm:

• The ACIDA algorithm works by extracting articulatory features from speech data using statistical and geometric techniques. It involves several steps, including data collection and preprocessing, articulatory feature extraction, and articulatory feature analysis.
• In the data collection and preprocessing step, the MOCHA-TIMIT database is used, and the data is preprocessed to remove noise and align the audio and articulatory signals.
• The articulatory feature extraction step involves the ACIDA algorithm itself. This algorithm combines statistical analysis and geometric modeling to extract articulatory features from the data. It identifies critical coordinates and their phonetic analysis, providing insights into the articulatory movements involved in speech production.
• The extracted articulatory features are then analyzed and compared with expected critical coordinates, demonstrating good agreement. This analysis helps validate the effectiveness of the ACIDA algorithm in capturing relevant articulatory information.

Impact

The evolution of neural network models have ushered in transformative impacts across various facets of speech synthesis, notably enhancing the quality, expressiveness, and versatility of synthesized speech.

Enhanced Quality of Speech Synthesis

Neural network-based vocoders, such as WaveNet, have significantly improved the quality of synthesized speech, providing more natural and expressive voice outputs. This has been pivotal in reducing the robotic tones often occurs in earlier TTS systems. Using neural network techniques like Tacotron 2 and WaveNet, we can process transcript-free noisy speech datasets in more precise way. Based on it we can create a model capable of generating audio in speakers' voices that not present in the original data.[1]

Prosody Modeling

The emergence of neural network models in speech synthesis has dramatically influenced prosody modeling, which involves the prediction and generation of prosodic features like pitch, duration, and energy. It is crucial for producing speech that sounds rhythmic and sentimental. It enables the synthesis of expressive and emotional speech by learning and generating varied prosody, which is crucial for conveying different emotions and speaking styles. Neural network based end-to-end text to speech can facilitate the development of controllable speech synthesis systems where prosody can be manipulated to generate speech with desired pitch, stress, and rhythm.

End-to-End Systems

Neural network-based vocoders on End-to-End (E2E) Systems can directly convert text to speech without requiring intermediate representations. It enables systems to learn complex mappings from input to output and often simplifying the traditional multi-stage processing pipeline. Tacotron, an end-to-end generative text-to-speech model can achieve speech synthesis directly from characters, which significantly reduces requirements of expertise of acoustic and many other domain.This has also enabled the creation of speech that can be more dynamically adjusted to various contexts and emotional tones, enhancing applications like virtual assistants and conversational agents.

Generative Modeling

Neural networks have enabled the development of generative models that can produce high-quality, natural-sounding speech, improving upon traditional concatenative and parametric methods. It can generate speech with varied emotional content, and can be trained to mimic different speakers, accents, and styles, providing versatility in speech synthesis applications. Generative models have advantages in situations with limited training data to synthesize speech, enabling the creation of voices for speakers with limited available recordings.

Future research

Multi-Modal Speech Synthesis

Multi-modal speech synthesis refers to the generation of synthetic speech that is not only audible but also visually coherent with facial movements, as we mentioned before in Key Innovations: articulatory features-based TTS. Neural network models, especially generative models like Generative Adversarial Networks (GANs), have been pivotal in synthesizing realistic visual representations (like lip movements) corresponding to synthesized or real speech.

Advantages:

• Enhanced User Experience: Multi-modal synthesis provides a richer and more immersive user experience by aligning visual cues with synthesized speech.
• Accessibility: It can enhance communication accessibility, especially for individuals with hearing impairments, by providing visual speech cues.
• Realistic Virtual Interactions: It enables the creation of realistic virtual characters or digital humans for applications in virtual reality, gaming, and online communication.

Challenges:

• Lip Synchronization: Ensuring that the synthesized speech is perfectly synchronized with the lip movements to avoid uncanny valley experiences.
• Expressiveness: Maintaining natural facial expressions and emotions while ensuring lip synchronization can be complex.
• Data Requirements: Acquiring high-quality, synchronized audio-visual data for training models can be challenging and resource-intensive.
• Computational Complexity: Managing and processing multiple modalities (audio and visual) requires significant computational resources and optimized algorithms.

Efficient speech synthesis

Achieving high-quality speech synthesis propels us towards the pivotal task of efficient synthesis, which encompasses minimizing the costs associated with speech synthesis, such as data collection, labeling, and TTS model training and serving.

Modern neural TTS systems, while capable of synthesizing exquisite speech, typically utilize substantial neural networks, often inhibiting applications in resource-constrained devices like mobiles and IoT due to their extensive memory and power demands. Thus, crafting models that are both compact and lightweight, ensuring reduced memory usage, power consumption, and latency, becomes imperative for such applications.

Moreover, the energy-intensive and carbon-emitting nature of training and serving top-tier TTS models necessitates enhancements in energy efficiency, such as diminishing the FLOPs in TTS training and inference, to broaden accessibility to advanced TTS technologies while concurrently mitigating environmental impact.

Challenges:

• Balancing Quality and Efficiency: Crafting models that are lightweight yet do not compromise on the quality of speech synthesis.
• Adaptability: Ensuring that efficient models can adapt to various speakers, emotions, and styles with limited resources.
• Energy-Efficient Training: Developing training methodologies that require less computational power without sacrificing the learning capability of the models.
• Low-Resource Adaptation: Ensuring the models can perform optimally even in environments with restricted computational and memory resources.
• Environmental Sustainability: Aligning the development and usage of TTS technologies with environmental sustainability goals, ensuring that advancements do not exacerbate carbon emissions.

Cross-Lingual and Multi-Lingual Speech Synthesis:

Nowadays, most of the speech synthesis model is still based on one single language system, or that user can switch to different mode to change speaking language. It is possible to create models that can seamlessly synthesize speech across multiple languages, without users to switch the language mode manually.

The challenge of it is about handling the varied phonetic, prosodic, and syntactic nuances across different languages without letting those phonetic systems interfere each others.

Team Members

Qing Li

Lifan Qu

Yi Lei