Mati Staniszewski: Modern audio models replicate human speech using neural networks, the importance of text and voice characteristics, and Eleven Labs’ mission to transform business communication

Key Takeaways

Audio models replicate human speech using phonemes and contextual predictions.
Modern audio models leverage neural networks for sound prediction.
Voice models require text and voice characteristics for accurate vocalization.
Advanced voice models can deduce characteristics like accent and enthusiasm.
Generating human-like speech involves both phoneme and text processing.
Speech model quality depends on architecture, compute power, and data quality.
Eleven Labs focuses on transforming business communication with audio models.
AI model integration is crucial for effective business operations.
Voice interaction technology lags behind the capabilities of current models.
Significant advancements in automotive voice models are expected this year.
Eleven Labs builds foundational models for business communication transformation.
The deployment gap in voice technology affects daily user experiences.
Staying updated with AI technology is critical for operational success.
Voice models are evolving to predict sounds based on context.
The automotive industry will see improved voice model integration soon.

Guest intro

Mati Staniszewski is the co-founder and CEO of ElevenLabs, an AI audio startup valued at 11 billion dollars that specializes in developing natural-sounding speech synthesis software. Prior to founding ElevenLabs in 2022, he worked as a Deployment Strategist at Palantir Technologies, where he managed large-scale implementation projects across public and private sectors. Under his leadership, ElevenLabs has become the leading company in voice AI, enabling audio to be accessible across languages and voices while capturing the humanness of speech through realistic emotional inflection.

How audio models replicate human speech

Audio models work by replicating human speech through phonemes and predictions.
In early days you try to replicate it exactly like you would replicate it with the human body… you would try to stitch in phonemes effectively different sounds of how we speak humans and then try to concatenate them together.

— Mati Staniszewski
Modern models use neural networks to predict sounds based on context.
Now we effectively do similar like neural nets in in other domains so you predict the next sound based on on of course the context of the previous sounds.

— Mati Staniszewski
Understanding phonemes is crucial for speech synthesis.
The evolution from earlier methods to neural networks marks significant progress.
These models require both text and voice characteristics for accuracy.
When you actually try to vocalize something when you create that voice model you turn text into audio you need the text you also need the voice reference of how you want it to to to be spoken.

— Mati Staniszewski

The dual requirements of voice modeling

Voice models need text and voice characteristics for effective vocalization.
When you actually try to vocalize something when you create that voice model you turn text into audio you need the text you also need the voice reference of how you want it to to to be spoken.

— Mati Staniszewski
The ability to deduce voice characteristics is a significant innovation.
The model will deduce them themselves the same for other set of parameters that are not hardcoded whether it’s the enthusiasm whether it’s the subness etcetera.

— Mati Staniszewski
This innovation shifts from hardcoded parameters to dynamic modeling.
Understanding traditional voice modeling limitations is essential.
The complexity of the technology highlights its advanced nature.
These advancements mark a shift towards more natural voice interactions.

Generating human-like speech with dual approaches

Human-like speech generation involves phoneme and text level operations.
When you are predicting the context you need to understand yes how that sentence will get constructed and especially if it’s more of a streaming real time use case and like a voice agent setting you need both parts to to work across.

— Mati Staniszewski
Real-time applications require integrated phonetic and textual elements.
The quality of speech models depends on architecture, compute power, and data.
In any model you need you need architecture you need compute you need data.

— Mati Staniszewski
Understanding machine learning model development is crucial.
These components provide a framework for effective speech model development.
The complexity of integrating phonetic and textual elements is necessary for realism.

Eleven Labs’ mission in audio and voice technology

Eleven Labs builds foundational audio and voice models for businesses.
In like the nutshell describe eleven labs is a research and product deployment company we build foundational audio and voice models and then build a platform for businesses to transform how they communicate with their customers with their employees.

— Mati Staniszewski
The company focuses on transforming business communication.
Understanding the role of audio technology in business is essential.
Eleven Labs aims to enhance communication with customers and employees.
The company’s mission highlights its focus on innovation in audio technology.
This approach positions Eleven Labs as a leader in voice technology.
The integration of these models with business applications is crucial.

The importance of AI model integration in business

Integrating AI models with business applications is crucial for operations.
It’s one thing you know with saas where you get these like vertical specific providers but I would imagine one of the biggest risks for you guys in being intermediated is if there’s you know like in this example a closed captioning service that is on a two versions old version of 11 and hasn’t upgraded that’s a problem because you want people to be using the latest and greatest model that you’ve developed.

— Mati Staniszewski
Staying updated with technology is critical to avoid risks.
This insight highlights the importance of using the latest AI models.
Businesses must keep up with rapid AI technology evolution.
The risk of outdated technology can impact business operations.
Effective operations require the latest AI model integration.
This integration is essential for leveraging AI advancements.

The deployment gap in voice technology

Voice interaction technology lags behind current model capabilities.
I agree with the premise that we are ten years behind in the lived experience of people day to day… there is definitely a piece of like the like we… I think the technology in many of those cases already there’s a deployment gap.

— Mati Staniszewski
The deployment gap affects daily user experiences.
Understanding the current state of voice technology is crucial.
This gap indicates a significant issue in technology adoption.
Advanced voice models are not fully utilized in everyday applications.
The lag highlights the need for better deployment strategies.
Bridging this gap is essential for improving user experiences.

Advancements in automotive voice models

Significant advancements in automotive voice models are expected this year.
I think this year it should be in the automotive side too or the some of the applications that we’ve seen we’ll start seeing kind of great voice models in cars this year.

— Mati Staniszewski
The automotive industry will see improved voice model integration.
Understanding current voice technology in automotive applications is important.
This prediction indicates a trend in the automotive industry.
The integration of advanced voice models in cars is a key development.
These advancements will enhance automotive user experiences.
The automotive sector is poised for significant voice technology growth.