Next Generation Speech Synthesis and Speech-to-Speech Technologies

Introduction

Introduction

Hello, my name is Ishai Carmel, and today I'm going to discuss the future of voice, specifically focusing on the next generation of speech synthesis and speech-to-speech technologies.

My Background

I've been working in human language understanding for more than 15 years, particularly in speech technologies. I started working on deep learning for speech a little over 10 years ago. Recently, I have noticed a significant paradigm shift from analysis to synthesis in AI.

From Analysis to Synthesis

What Does This Shift Mean?

Previously, AI was primarily used to analyze data generated by humans, such as object recognition in computer vision, text analysis, and speech recognition for transcription. However, we are now seeing a shift towards AI synthesis, where AI generates new types of data. Examples include stable diffusion models generating new types of art, DALL-E generating new types of content creation for text, and large language models creating new forms of text content.

What's Happening with Voice?

Voice is a crucial data type we use daily alongside images and text. The key question is how generative AI will impact voice.

Types of Voice Technologies

There are two main voice technologies that have a significant impact:

1. Text-to-Speech (TTS): This technology has been around for a while and is continuously improving.
2. Speech-to-Speech: This is a fascinating emerging technology that focuses on transforming existing human speech into new types of speech.

Types of Speech Applications

All speech applications can be broadly categorized into three buckets:

1. Speech Recognition: The ability of a machine to analyze human voice and transform it into text.
2. Speech Profiling: Extracting metadata from speech, such as verifying a person's identity, identifying the language spoken, or detecting the emotional state of the speaker.
3. Speech Synthesis: The ability of a machine to generate human voice.

Text-to-Speech

How Text-to-Speech Works

Text-to-Speech systems aim to take a given text and convert it into voice. The two main blocks involved are:

1. Natural Language Understanding (NLU): Takes the text and converts it into another form of text that serves as input for speech synthesis.
2. Speech Synthesis Systems: These systems further convert the processed text into a speech representation and finally into the output speech.

Challenges and Future of Text-to-Speech

Text-to-Speech systems can currently provide clean voice with natural intonation for read speech. However, the challenges lie in:

1. Conversational Speech: People speak differently in conversation than when reading a sentence.
2. Emotional Speech: Making TTS systems sound more human-like with varied emotions.

Speech-to-Speech

How Speech-to-Speech Works

Speech-to-Speech takes one voice and converts it into another voice without going through the word space. This avoids stripping down 80% of the information and reduces recognition errors.

Applications

Applications include:

1. Voice Conversion: Making one speaker sound like another.
2. Movie Dubbing: Converting movies into different languages using the same actor's voice.

Here's an example of voice conversion:

• Source Speaker: "I've done nothing wrong and that's the truth."
• Target Speaker: "He felt there was no case to answer."
• Converted Speech: "I've done nothing wrong and that's the truth," in the target speaker's voice.

Self-Supervision in Speech

Self-Supervised Learning

Inspired by advancements in text (like BERT in 2018), researchers applied similar techniques to speech. Meta researchers created Hubert to represent speech better. This led to significant improvements in various applications, such as:

1. Voice Compression: New algorithms are 25 times more efficient while providing higher quality.
2. Emotional Transfer: Transforming neutral speech into speech with different emotions.

Conclusion

I discussed three major areas today:

1. Text-to-Speech: A well-established technology continuously improving.
2. Speech-to-Speech: An emerging technology with significant potential for new applications.
3. Self-Supervision for Speech: Holds great promise and is attracting substantial attention.

The future of these technologies looks very promising, with many interesting applications on the horizon. Thank you for listening. I hope you enjoyed the talk.

Keywords

• Speech Synthesis
• Text-to-Speech (TTS)
• Speech-to-Speech
• Natural Language Understanding (NLU)
• AI Synthesis
• Generative AI
• Voice Conversion
• Self-Supervised Learning
• Hubert
• Emotional Transfer
• Voice Compression

FAQs

Q: What is the paradigm shift in AI?
A: The paradigm shift in AI is moving from analysis to synthesis. Previously, AI analyzed human-generated data (e.g., object recognition, text analysis), but now it is generating new types of data (e.g., creating art, generating text).

Q: What are the main voice technologies?
A: The main voice technologies are Text-to-Speech (TTS) and Speech-to-Speech.

Q: What is the difference between Text-to-Speech and Speech-to-Speech?
A: Text-to-Speech converts written text into spoken words, while Speech-to-Speech transforms existing human speech into new types of speech without converting to text.

Q: What are the current challenges with Text-to-Speech technology?
A: The main challenges are making TTS systems handle conversational speech and generating emotional speech to sound more human-like.

Q: What is self-supervised learning in speech?
A: Self-supervised learning in speech involves creating new speech representations using techniques inspired by advancements in text-based models like BERT. An example is Meta's Hubert.

Q: Can speech-to-speech technology convert one person's voice to sound like another?
A: Yes, speech-to-speech technology can convert a source speaker's voice to sound like a target speaker's voice.