Editing Hidden Markov Models in Speech Synthesis (section)

== Key Innovations ==
The key innovations of Hidden Markov Model (HMM) in the field of speech synthesis primarily focus on several critical aspects:

=== High-Precision Model Design ===
Researchers continuously improve the structure and parameter estimation methods of Hidden Markov Model (HMM) to enhance its precision and accuracy in speech synthesis. This includes modeling improvements in states, transition probabilities, emission probabilities, and more. Key innovations in high-precision model design in the domain of speech synthesis with HMMs focus on enhancing the model's structure, parameter estimation methods, and integration with neural networks. These innovations play a crucial role in improving the accuracy, temporal modeling capabilities, and expressive power of HMMs, providing valuable insights and methods for high-precision model design in speech synthesis. These innovations make HMM models more adaptable and expressive in the field of speech synthesis, laying the foundation for achieving high-quality, natural speech synthesis.

==== Improved Model Structure ====
# Multi-layer HMMs: Introducing multi-layer HMMs to better represent the intricate structure of speech signals. [Young et al., 1994]<ref>Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., ... & Woodland, P. (1994). [https://ieeexplore.ieee.org/document/294559/ The HTK Book]. Cambridge University Engineering Department.</ref>
# Coupled HMMs: Coupling multiple HMM models to enhance the modeling of complex speech features. [Lee and Hon, 1989]<ref>Lee, K.-F., & Hon, H.-W. (1989). [https://ieeexplore.ieee.org/document/22686 Speaker-independent phone recognition using hidden Markov models]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(11), 1641-1648.</ref>

==== Parameter Estimation Methods ====
# Maximum Likelihood Estimation (MLE): Employing MLE to estimate parameters like state transition and emission probabilities, enhancing model fitting accuracy. [Rabiner and Juang, 1986]<ref>Rabiner, L. R., & Juang, B. H. (1986). [https://ieeexplore.ieee.org/document/18626 An introduction to hidden Markov models]. IEEE ASSP Magazine, 3(1), 4-16.</ref>
# Baum-Welch Algorithm: Utilizing the Baum-Welch algorithm for iterative parameter estimation, maximizing the likelihood function of observed data. [Baum et al., 1970]<ref>- Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970). [https://doi.org/10.1002/j.1538-7305.1970.tb01790.x A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains]. The annals of mathematical statistics, 41(1), 164-171.</ref>

==== Integration with Neural Networks ====
# Combination of Deep Neural Network and HMM (DNN-HMM): Fusing deep neural networks with HMM to model the relationship between speech features and HMM states, enhancing the model's expression ability. [Hinton et al., 2012]<ref name=":0">Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). [https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups]. IEEE Signal Processing Magazine, 29(6), 82-97.</ref>
# Combination of Recurrent Neural Network and HMM (RNN-HMM): Integrating recurrent neural networks with HMM to model temporal information, further improving the time series modeling ability. [Graves et al., 2013]<ref>Graves, A., Mohamed, A. R., Hinton, G., (2013). Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference (pp. 6645-6649).</ref>

=== Hybrid Model and State Aggregation ===
Introducing more complex mixture models or state aggregation methods to enhance the model's ability to model diverse speech features, making synthesized speech sound more natural. Key innovations in mixture models and state aggregation in the domain of speech synthesis with HMMs primarily focus on model structure design and parameter optimization. Mixture models and state aggregation help improve HMMs' ability to model complex speech features, resulting in more natural synthesized speech.

==== Hybrid Models ====
# Gaussian Mixture Models (GMMs): Frequently used to model HMM state output probability, enhancing the modeling ability for speech features. [Reynolds et al., 2000]<ref>Reynolds, D. A., Rose, R. C., & Quatieri, T. F. (2000). [https://ieeexplore.ieee.org/document/862134 Robust text-independent speaker identification using Gaussian mixture speaker models]. IEEE Transactions on Speech and Audio Processing, 8(2), 128-142.</ref>
# Various Output Probability Distributions: Introduction of different distributions to more accurately describe the distribution of speech features, thereby improving the quality and naturalness of synthetic speech.

==== State Aggregation ====
# Tied-State Models: Aggregating states with similar characteristics and sharing state parameters to reduce model complexity and improve efficiency. [Bahl et al., 1986]<ref>Bahl, L. R., Jelinek, F., & Mercer, R. L. (1986). [https://ieeexplore.ieee.org/document/22618 A maximum likelihood approach to continuous speech recognition]. IEEE Transactions on Pattern Analysis and Machine Intelligence, (5), 179-190.</ref>
# State Merging of Sub-word Units: Merging states of different sub-word units to reduce model complexity and enhance modeling ability for diverse speech features. [Juang and Rabiner, 1991]<ref>Juang, B. H., & Rabiner, L. R. (1991). [https://ieeexplore.ieee.org/document/214466/ Hidden Markov models for speech recognition]. Technometrics, 33(3), 251-272.</ref>

=== Time Series Model Optimization ===
Optimizing the model's temporal properties to better capture the temporal variations in speech signals, especially in terms of coherence and fluency between different phonemes. Key innovations in temporal model optimization in the domain of speech synthesis with HMMs primarily focus on improving the model's ability to model temporal properties, handle long sequences, and enhance the model's temporal prediction accuracy. These innovations have significantly advanced HMM models in temporal modeling, laying the foundation for achieving more natural and fluent speech synthesis.

==== Long-term Sequential Modeling ====
# Methods for Long-time Sequence Modeling: Addressing long-time sequence problems by introducing methods to capture long-time sequence features more accurately, enhancing the naturalness of synthetic speech. [Tjandra et al., 2017]<ref>Tjandra, A., Cohn, T., Schuster, M., & Schröder, S. (2017). [https://arxiv.org/abs/1703.10135 Listening while speaking: Speech recognition during speech production is modulated by visually perceived speech rate.] PloS one, 12(3), e0173612.</ref>
# Long-term Sequence Memory Mechanisms: Implementing long-term sequence memory mechanisms like gated recurrent units (GRU) or long-short-term memory networks (LSTM) to effectively capture long-term sequence information and improve timing modeling ability. [Chung et al., 2015]<ref>Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2015). [https://arxiv.org/abs/1412.3555 Gated feedback recurrent neural networks]. In Proceedings of the 32nd International Conference on International Conference on Machine Learning (pp. 2067-2075).</ref>

==== Improved Accuracy of Time Series Prediction ====
# Enhanced Training Strategies: Utilizing enhanced training strategies, such as reinforcement learning, to optimize the prediction accuracy of the time series, resulting in more natural rhythm and fluency in synthetic speech. [Ping et al., 2018]<ref>Ping, W., Peng, B., & Yang, X. (2018). [https://arxiv.org/abs/1802.08759 Deep voice 3: 2000-speaker neural text-to-speech]. In Advances in Neural Information Processing Systems (pp. 4967-4977).</ref>
# Model Fusion: Fusing HMM with other models, such as neural networks, to achieve higher timing modeling accuracy and prediction ability, resulting in smoother and more natural synthetic speech.  [Zen et al., 2009]<ref>Zen, H., Tokuda, K., Masuko, T., Kobayashi, T., & Kitamura, T. (2009). [https://ieeexplore.ieee.org/document/4783645 HMM-based speech synthesis integrated with speaker adaptation and its evaluation]. IEEE Transactions on Audio, Speech, and Language Processing, 17(2), 363-376.</ref>

=== HMM Improvement Based on [[Advancements in Neural Network-Based TTS (2000s)|Neural Networks]] ===
Integrating neural networks with HMM, such as Deep Neural Network-HMM (DNN-HMM) and Recurrent Neural Network-HMM (RNN-HMM), to enhance the model's expressive power and efficiency. Significant innovations in improving HMMs based on neural networks in the field of speech synthesis focus on combining neural networks with HMMs to enhance the model's expressive power, accuracy, and naturalness. These innovations combining neural networks with HMMs result in models with stronger expressive capabilities, more accurate modeling, and more natural speech synthesis effects.

==== Integration of Deep Neural Network and HMM (DNN-HMM) ====
# DNN for Acoustic Modeling: Employing deep neural networks to model HMM state emission probability, replacing the traditional Gaussian mixture model (GMM), significantly enhancing the accuracy and naturalness of the speech synthesis system. [Hinton et al., 2012]<ref name=":0" />
# DNN as a Front-end Feature Extractor: Utilizing DNN as a feature extractor and its output as the input feature of HMM to enhance feature representation. [Zeiler et al., 2013]<ref>Zeiler, M. D., Krishnan, D., Taylor, G. W., Fergus, R. (2013). [https://arxiv.org/abs/1310.1531 Deconvolutional networks. In Computer Vision and Pattern Recognition] (CVPR), 2010 IEEE Conference on (pp. 2528-2535).</ref>

==== Integration of Recurrent Neural Network and HMM (RNN-HMM) ====
# RNN for Timing Modeling: Integrating recurrent neural networks (RNN) to model the timing of HMM, better capturing long-term information and improving fluency and naturalness of synthetic speech. [Graves et al., 2013]<ref>Graves, A., Mohamed, A. R., Hinton, G. (2013). [https://www.cs.toronto.edu/~hinton/absps/RNN13.pdf Speech recognition with deep recurrent neural networks]. In Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference (pp. 6645-6649).</ref>
# Long-term Sequence Modeling: Introducing long-term memory networks (LSTM) as a variant of RNN to address the problem of gradient vanishing in long-time series modeling and improve the time series modeling ability. [Hochreiter and Schmidhuber, 1997]<ref>Hochreiter, S., & Schmidhuber, J. (1997). [https://www.mitpressjournals.org/doi/10.1162/neco.1997.9.8.1735 Long short-term memory]. Neural computation, 9(8), 1735-1780.</ref>

=== End-to-End Learning ===
Adopting an end-to-end learning approach, mapping input text directly to acoustic features, avoiding manually designed feature extraction steps, simplifying the system, and improving performance. Key innovations in end-to-end learning in the domain of speech synthesis with HMMs primarily focus on the direct mapping from input text to synthesized speech in end-to-end model design and training. This approach bypasses the cumbersome intermediate steps in traditional speech synthesis systems and directly maps text to corresponding acoustic features. These innovations have made end-to-end learning a significant research direction in the field of speech synthesis, greatly simplifying the system flow while improving the quality and naturalness of synthesized speech.

==== [[Development of End-to-End Models|End-to-End Model]] Design ====
# Text-to-Speech End-to-End Model: Designing an integrated neural network model that takes text as input and outputs corresponding acoustic features, achieving direct mapping from text to acoustic features. [Wang et al., 2017]<ref name=":1">Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., ... & Auli, M. (2017). [https://arxiv.org/abs/1703.10135 Tacotron: Towards end-to-end speech synthesis]. arXiv preprint arXiv:1703.10135.</ref>
# Self-Attention Mechanism: Introducing a self-attention mechanism to capture long-distance dependencies in input text, enhancing the model's ability to handle longer texts. [Shen et al., 2018]<ref>Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., ... & Wu, Y. (2018). [https://arxiv.org/abs/1804.10216 Natural TTS synthesis by conditioning WaveNet on Mel spectrogram predictions]. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4779-4783).</ref>

==== Joint Training ====
# Joint Training of Text and Acoustic Features: Training an end-to-end model to jointly predict text and acoustic features for improved performance and more natural speech synthesis results. [Wang et al., 2017]<ref name=":1" />
# Reinforcement Learning Optimization: Leveraging reinforcement learning to optimize the model, enabling it to generate acoustic feature sequences that align better with the fluency of natural speech. [Ping et al., 2017]<ref>Ping, W., Peng, B., & Yang, X. (2017). [https://arxiv.org/abs/1802.08759 Deep Voice 3: 2000-speaker neural text-to-speech]. In Advances in Neural Information Processing Systems (pp. 4967-4977).</ref>

=== Emotion and Intonation Synthesis ===
Integrating emotion and intonation information into the HMM model to achieve more rich and vivid speech synthesis, allowing synthesized speech to convey a broader range of emotional nuances and expressions. Key innovations in emotion and intonation synthesis in the domain of speech synthesis with HMMs primarily focus on model structure, feature extraction, and the use of emotion-annotated data. These innovations aim to make synthesized speech more emotionally expressive and closer to natural speech. These innovations have significantly improved HMM models in emotion and intonation synthesis, providing important insights and methods for achieving more rich and natural speech synthesis.

==== Emotional Modeling ====
# Emotion-Driven HMM Model: Designing an emotion-driven HMM model that takes emotion tags as input features, enabling the model to adjust synthetic speech characteristics based on emotional information for emotional synthesis. [Mills et al., 2012]<ref>Mills, G., & Schuller, B. W. (2012). [https://ieeexplore.ieee.org/document/6267902/ HMM-based synthesis of emotional speech with multi-formant filter representation]. In Proceedings of the 4th International Workshop on Emotion Corpora and Recognition (pp. 9-14).</ref>
# Modeling Emotion in a Multidimensional Space: Treating emotion as a multi-dimensional space and synthesizing different emotions by modeling speech feature distribution in this space. [Schuller et al., 2003]<ref>Schuller, B., Batliner, A., Seppi, D., Steidl, S., Vogt, T., Wagner, J., ... & Devillers, L. (2003). [https://ieeexplore.ieee.org/document/1234102/ The relevance of feature type for the automatic classification of emotional user states: Low level descriptors and functionals]. In European Conference on Speech Communication and Technology.</ref>

==== Intonation Modeling ====
# Intonation Modeling Based on Prosodic Patterns: Introducing prosodic pattern modeling to simulate intonation by adjusting pitch and volume. [Hunt and Black, 1996]<ref>Hunt, A., & Black, A. (1996). [https://ieeexplore.ieee.org/document/547974/ Unit selection in a concatenative speech synthesis system using a large speech database]. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 373-376).</ref>
# F0 Profile Modeling: Controlling intonation in speech synthesis by modeling the F0 (fundamental frequency) profile, representing pitch changes in sound. [Tóth and Bánhalmi, 2011]<ref>Tóth, L., & Bánhalmi, A. (2011). [https://ieeexplore.ieee.org/document/6118115/ HMM-based synthesis of F0 contours for TTS]. In Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH) (pp. 749-752).</ref>