Learn How to Create a Finnish Accent Speech Synthesizer in C++17

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Learn How to Create a Finnish Accent Speech Synthesizer in C++17

Table of Contents

  1. Introduction
  2. Background Information
  3. Phonemes: Building Blocks of Speech
  4. Creating Phoneme Pairs for Speech Synthesis
  5. Recording Voice Samples for Speech Synthesis
  6. The Source-Filter Model: Adding Resonance and Noises
  7. Linear Predictive Coding (LPC)
  8. Choosing the Order for LPC Data
  9. Adjusting Parameters for Synthesis
  10. Converting Text into Phonemes
  11. Improving Speech Quality: Pitch, Variation, and Flow
  12. Handling Clicks and Pops in Audio
  13. Making the Speech Synthesizer Read English
  14. Conclusion

Introduction

In this article, we will explore the process of creating an English speech synthesizer with a Finnish accent. We will Delve into the intricacies of speech synthesis, including the use of phonemes as building blocks of speech, the recording of voice samples, and the implementation of the source-filter model. We will also discuss the concept of Linear Predictive Coding (LPC) and how it can be used in speech synthesis. Additionally, we will address various aspects of the synthesizer, such as adjusting parameters, handling clicks and pops in audio, and making it read English. So, let's dive in and uncover the fascinating world of speech synthesis!

Background Information

Before we delve into the technicalities of speech synthesis, it's essential to lay the groundwork by providing some background information. In a series of videos, the creator of this speech synthesizer explains the Context and concepts related to the project. If You haven't already watched these videos, it is highly recommended to do so before proceeding with this article. They provide crucial insights into the development process and set the stage for our exploration of the speech synthesizer. To access the video playlist, click the card provided and familiarize yourself with the content.

Phonemes: Building Blocks of Speech

When it comes to speech synthesis, understanding phonemes is crucial. Phonemes are the smallest units of sound that make up words in language. In the case of the speech synthesizer we're creating, we will focus on the phonemes used in Finnish speech, as they contribute to the desired accent. However, it's important to note that some phonemes in Finnish are not pronounced correctly by most Finns; instead, they are aliased into other phonemes to maintain authenticity and keep the design simple. In this article, we will explore the roster of 22 phonemes used in the synthesizer and their significance in the synthesis process.

Creating Phoneme Pairs for Speech Synthesis

To Create a high-quality speech synthesizer, it is common practice to generate a list of all possible pairs of phonemes that can occur in normal speech. This includes combinations of consonants followed by vowels, vowels followed by consonants, pairs of vowels, and pairs of consonants. In the ideal Scenario, a voice artist would be hired to Record hundreds of samples representing these phoneme pairs at a constant pitch and stress level. However, for the purposes of our demo speech synthesizer, we will simplify the process by operating on single phonemes only. By using recordings of ourselves speaking the different phonemes, we can generate the necessary samples more efficiently. Let's explore the practical implementation of this approach in the next section.

Recording Voice Samples for Speech Synthesis

To obtain the required voice samples for our speech synthesizer, we can rely on our own recordings of the phonemes. By speaking each phoneme individually and recording them, we can create a library of sounds to be used in the synthesis process. This approach offers flexibility and ease of implementation, as we can quickly generate the necessary samples without the need for extensive recording Sessions or the hiring of a voice artist. However, it's important to ensure Clarity and accuracy in the recordings to achieve optimal results. In the subsequent sections, we will explore the utilization of the recorded voice samples in the speech synthesis process.

The Source-Filter Model: Adding Resonance and Noises

In the synthesis of speech, the source-filter model is a fundamental concept. This model comprises a sound source and a tube that introduces resonants and noises to the sound. In the context of our speech synthesizer, the recorded voice samples serve as the source, while the tube represents the filter that adds resonance and noises. By applying the filter to the source, we can Shape the frequency characteristics of the sound, resulting in the synthesis of speech. The source-filter model is widely used in audio compression methods, such as Linear Predictive Coding (LPC), which we will explore in more Detail later.

Linear Predictive Coding (LPC)

Linear Predictive Coding (LPC) is a technique commonly used in speech synthesis. It is Based on the source-filter model and operates by assuming that a speech signal is produced by a buzzer at the end of a tube, with occasional added hissing and popping sounds. LPC involves analyzing the speech signal to determine the coefficients and gain that best represent the recorded sound. These coefficients and gain values are then used to synthesize speech by applying the filter to the buzzer-like source. In this section, we will delve deeper into the workings of LPC and its significance in the speech synthesis process.

Choosing the Order for LPC Data

In the implementation of LPC, the order of the data plays a crucial role in achieving the desired audio quality. The order refers to the number of coefficients used to represent the speech signal. In our speech synthesizer, we have experimented with different orders for LPC data and have compared the resulting audio quality. The order significantly affects the accuracy of the synthesis and the occurrence of artifacts in the output. Through a comprehensive analysis, we have determined the optimal order for our speech synthesizer. Let's explore the factors involved in choosing the order and the impact it has on the synthesis process.

Adjusting Parameters for Synthesis

To fine-tune the performance of our speech synthesizer, it is essential to adjust various parameters that govern the synthesis process. These parameters include breathiness, buzziness, and pitch, among others. By manipulating these parameters, we can enhance the quality and clarity of the synthesized speech. Additionally, adjusting the parameters allows us to create variation and add interest to the voice output. In this section, we will discuss the different parameters and their effects on the synthesis process, ultimately leading to a more natural and engaging speech output.

Handling Clicks and Pops in Audio

One common issue in speech synthesis is the occurrence of clicks and pops in the audio output. These artifacts can be disruptive and negatively impact the listener's experience. In our speech synthesizer, we have encountered similar challenges and developed workarounds to mitigate this problem. While the exact cause of these artifacts remains unclear, we have identified techniques that can effectively address and minimize clicks and pops in the synthesized speech. In the following section, we will share our findings and provide insights into handling these audio artifacts.

Making the Speech Synthesizer Read English

While our speech synthesizer was initially designed to produce speech with a Finnish accent, we have explored adapting it to read English text. To achieve this, we borrowed code from an existing speech synthesis program and modified it to convert English text into a set of phonemes represented in the International Phonetic Alphabet. We then mapped these phonemes to their Finnish counterparts, allowing the synthesizer to pronounce English words with a Finnish accent. In this section, we will delve into the process of converting English text into phonemes and discuss the nuances involved in achieving an authentic Finnish accent.

Conclusion

In conclusion, the creation of an English speech synthesizer with a Finnish accent is a complex and multifaceted endeavor. It involves understanding the building blocks of speech (phonemes), recording voice samples, implementing the source-filter model, and utilizing techniques like Linear Predictive Coding. By adjusting parameters, handling artifacts, and adapting to read English text, we can achieve a realistic and engaging synthesis of speech. Through this article, we have explored the intricacies of speech synthesis and provided insights into the development process of our speech synthesizer. With a deep understanding of the underlying concepts and a commitment to ongoing refinement, we can Continue to improve and enhance the capabilities of our synthesizer. As technology advances and new developments emerge, the world of speech synthesis holds limitless possibilities for innovation and creativity.

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