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However, the state of the art is more diverse and complex. It contains many variants and hybrid approaches between them.

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This approach is based on the concatenation of pieces of audio signals corresponding to different phonemes. This method is segmented in several steps.


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First, the characters should be converted in the corresponding phones to be pronounced. A simplistic approach is to assume that one letter corresponds to one phoneme for example. Then the computer must know what signal corresponds to a phoneme.

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A possibility to solve this problem is to record a database containing all the existing phonemes in a given language. However concatenating phones one after another leads to very unnatural transitions between them. In the literature, this problem was tackled by recording successions of two phonemes, called diphones, instead of phones.

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All combinations of diphones are recorded in a dataset. The generation of speech is then performed by concatenation of these diphones. In this approach, many assumptions are not met in practice. First, a text processing has to be performed. Indeed, text is constituted of punctuation, numbers, abbreviations, etc.


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Moreover, the letter-to-sound relationship is not respected in English and in many other languages. The pronunciation of words often depend on the context. Also, concatenating phone leads to a chopped signal and prosody of the generated signal is unnatural. To have a control on expressiveness with diphone concatenation techniques, it is possible to change F 0 and duration with signal processing techniques implying some distorsion on the signal. Other parameters cannot be controlled without altering the signal leading to unnatural speech. Another approach that is also based on the concatenation of pieces of signal is Unit Selection.

Instead of concatenating phones or diphones , larger parts of words are concatenated. An algorithm has to select the best units according to criteria: few discontinuities in the generated speech signal, a consistent prosody, etc. For this purpose, a much larger dataset must be recorded containing a large variety of different combinations of phone series. The machine must know what part of signal corresponds to what phoneme, which means it has to be annotated by hand accurately.

This annotation process is time-consuming. Today, there exist tools to do this task automatically. But this automation can in fact be done at the same time as synthesis as we will see later. The advantages of this method is that the signal is less altered and most of the transitions between phones are natural because they are coming as is from the dataset. With this method, a possibility to synthesize emotional speech is to record a dataset with separate categories of emotion.

In synthesis, only units coming from a category will be used [ 8 ]. The drawback is that it is limited to discrete categories without any continuous control. Parametric Speech Synthesis is based on modeling how the signal is generated. It allows interpretability of the process. But in general, simplistic assumptions have to be made for speech modeling. Anatomically, the speech signal is generated by an excitation signal generated in the larynx. This excitation signal is transformed by resonance through the vocal tract, which acts as a filter constituted by the guttural, oral, and nasal cavities.

If this excitation signal is generated by glottal pulses, then a voiced sound is obtained. Glottal pulses are generated by a series of openings and closures of vocal cords or vocal folds. The vibration of the vocal chords has a fundamental frequency. As opposed to voiced sounds, when the excitation signal is a simple flow of exhaled air, it is an unvoiced sound. The source-filter model is a way to represent speech production, which uses the idea of separating the excitation and the resonance phenomenon in the vocal tract.

The Theory behind Controllable Expressive Speech Synthesis: A Cross-Disciplinary Approach

It assumes that these two phenomena are completely decoupled. The source corresponds to the glottal excitation and the filter corresponds to the vocal tract. This principle is illustrated in Figure 4 1. An example of Parametric Speech modeling is the linear prediction model.

The linear prediction LP model uses this theory assuming that the speech is the output signal of a recursive digital filter, when an excitation is received at the input. In other words, it is assumed that each sample can be predicted by a linear combination of the last p samples. The linear predictive coding works by estimating the coefficients of this digital filter representing the vocal tract. The number of coefficients to represent the vocal tract has to be chosen.

The more coefficients we take, the better the vocal tract is represented, but the more complex the analysis will be.

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The excitation signal can then be computed by applying the inverse filter on the speech signal. In synthesis, this excitation signal is modeled by a train of impulses. In reality, the mechanics of the vocal folds is more complex making this assumption too simplistic. The vocal tract is a variable filter. Depending on the shape we give to this vocal tract, we are able to produce different sounds. A filter is considered constant for a short period of time and a different filter has to be computed for each period of time.

This approach has been successful to synthesize intelligible speech but not natural human sounding speech. For expressive speech synthesis, this technique has the advantage of giving access to many parameters of speech allowing a fine control. The approach used in [ 9 ] to discover how to control a set of parameters to obtain a desired emotion was done through perception tests.

A set of sentences were synthesized with different values of these parameters. These sentences were then used in listening tests in which participants were asked to answer questions about the emotion they perceived. Based on these results, values of the different parameters were associated to the emotion expressions. It can be seen as Parametric Speech synthesis in which we take less simplistic assumptions on the speech generation and rely more on the statistics of data to explain how to generate speech from text.

The idea is to teach a machine the probability distributions of signal values depending on the text that is given. We generally assume that generating the values that are most likely is a good choice. We thus use the Maximum Likelihood principle see Section 4. These probability distributions are estimated based on a speech dataset. To be a good estimation of the reality, this dataset must be large enough.

The most recent statistical approach uses DNN [ 10 ], which is the basis of new speech synthesis systems such as WaveNet [ 11 ] and Tacotron [ 12 ]. The improvement provided by this technique [ 13 ] comes from the replacement of decision trees by DNNs and the replacement of state prediction HMM by frame prediction. In the rest of this chapter, we focus on this approach of Speech Synthesis.

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Section 4. Depending on the synthesis technique used [ 14 ], the voice is more or less natural and the synthesis parameters are more or less numerous. These parameters allow to create variations in the voice. The number of parameters is therefore important for the synthesis of expressive speech. While parametric speech synthesis can control many parameters, the resulting voice is unnatural.

Synthesizers using the principle of concatenation of speech segments seem more natural but allow the control of few parameters. The statistical approaches allow to obtain a natural synthesis as well as a control of many parameters [ 15 ]. Machine Learning consists of teaching a machine to perform specific task, using data. In this chapter, the task we are interested in is Controllable Expressive Speech Synthesis.

Deep Learning is the optimization of a mathematical model, which is a parametric function with many parameters. This model is optimized or trained by comparing its predictions to ground truth examples taken from a dataset. This comparison is based on a measure of similarity or error between a prediction and the true example of the dataset. The goal is then to minimize the error or maximize the similarity. This can always be formulated as the minimization of a loss function.

To find a good loss function, it is necessary to understand the statistics of the data we want to predict and how to compare them. For this, concepts from information theory are used. The form of the mathematical function used to process the signal can be constituted of lots of different operations.

Some of these operations were found very performant in different fields and are widely used. In this section, we describe some operations relevant for speech synthesis. In Deep Learning, the ensemble of the operations applied to a signal to have a prediction is called Architecture. There is an important research interest in designing architectures for different tasks and data to process.