[AI from Scratch] Episode 198: Speech Generation Models — Basics of Speech Synthesis Technology

Recap: Evaluation Metrics for Text Generation

In the previous episode, we discussed evaluation metrics for text generation, explaining key metrics like Perplexity and the BLEU score. Perplexity measures a model’s prediction accuracy, while the BLEU score evaluates the similarity between generated and reference texts. This time, we shift our focus to a related but distinct field: speech generation models.

What Are Speech Generation Models?

Speech generation models are technologies designed to synthesize natural speech from input information, such as text data. Speech synthesis is widely used in various applications, including smartphone voice assistants, navigation systems, and text-to-speech software.

There are several methods for speech generation models, but the main three approaches discussed here are:

1. Rule-Based Speech Synthesis
2. Unit Selection Speech Synthesis
3. Parametric Speech Synthesis

1. Rule-Based Speech Synthesis

Rule-Based Speech Synthesis is one of the earliest methods for generating speech. It combines pronunciation rules and phonemes to synthesize speech. However, this method requires extensive rules and struggles to produce natural-sounding speech, leading to the development of other techniques aimed at achieving more natural results.

2. Unit Selection Speech Synthesis

Unit Selection Speech Synthesis generates speech by selecting and combining pre-recorded phonemes (the smallest units of sound) from a large database of audio recordings. While this approach can improve the naturalness of the output, as it relies on recorded speech quality, it sometimes results in unnatural transitions between selected phonemes, leading to less smooth speech.

3. Parametric Speech Synthesis

Parametric Speech Synthesis represents speech characteristics (such as pitch, tone, and duration) as parameters and adjusts them to generate speech. This method offers greater flexibility in speech quality and pronunciation, but the output may sound somewhat mechanical.

Modern Approaches to Speech Generation: Neural Network-Based Speech Synthesis

In recent years, neural network-based speech synthesis technologies have rapidly developed, surpassing traditional methods in quality. Prominent approaches include WaveNet and Tacotron.

WaveNet

WaveNet is a neural network-based speech generation model developed by Google. It directly generates speech waveforms, resulting in significantly improved audio quality compared to traditional methods. WaveNet excels at creating highly realistic speech because it generates the actual waveforms of human speech, making the synthesized speech sound remarkably natural.

The detailed explanation of WaveNet will be covered in the next episode, but its ability to generate authentic waveforms is its standout feature, producing incredibly lifelike audio.

Tacotron

Tacotron generates speech by converting text into mel spectrograms (representations of speech features across time and frequency) and then converting these spectrograms into waveforms. When combined with WaveNet, Tacotron can achieve even higher-quality speech synthesis.

Applications of Speech Generation Models

Speech generation models are applied in various areas, including:

1. Voice Assistants

• Speech synthesis technology enables natural voice responses to user queries, as seen in Google Assistant and Amazon Alexa.

1. Navigation Systems

• These models provide spoken directions in car navigation and GPS devices.

1. Support for Visually Impaired Users

• Text-to-speech technology assists visually impaired individuals by reading out the content of books and web pages.

1. Entertainment

• Speech synthesis is used to create character voices in movies and games, adding realism to the presentation.

Evaluation Metrics for Speech Generation Models

To assess the quality of speech generation models, the following evaluation metrics are commonly used:

1. MOS (Mean Opinion Score)

• This subjective evaluation method involves human listeners rating the naturalness and quality of speech on a scale from 1 to 5, with 5 indicating the highest quality.

1. PESQ (Perceptual Evaluation of Speech Quality)

• An objective metric that quantifies speech quality. Like MOS, it aims to evaluate naturalness but does so automatically.

1. STOI (Short-Time Objective Intelligibility)

• This metric evaluates speech clarity and intelligibility, particularly useful for speech recognition tasks.

Summary

In this episode, we covered speech generation models. The field has evolved from rule-based methods to neural network-based approaches, dramatically enhancing speech quality. In the next episode, we will dive deeper into WaveNet, an advanced speech generation technology developed by Google that offers unprecedented naturalness in speech synthesis.

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Preview of the Next Episode

Next time, we will explain WaveNet in detail. WaveNet is a cutting-edge speech generation technology developed by Google, achieving a level of naturalness beyond traditional speech synthesis. Stay tuned!

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Annotations

1. Speech Generation Model: A model that generates speech from text or other input data.
2. Rule-Based Speech Synthesis: A method that uses pronunciation rules to generate speech.
3. Unit Selection Speech Synthesis: A method that combines pre-recorded phonemes from a large database.
4. Parametric Speech Synthesis: A method that generates speech by adjusting parameters representing speech characteristics.
5. WaveNet: A speech generation model developed by Google that directly generates speech waveforms.
6. Tacotron: A technology that generates mel spectrograms from text and converts them into speech.