How to use Speech AI systems for podcast hosting, editing, and monetization

The advances in Generative AI have impacted nearly every industry, with 9 out of 10 companies now turning to AI for a competitive advantage.

The $13.7 billion podcast industry is no exception. Podcast hosting, editing, and monetization platforms are exploring ways to leverage Speech AI transcription and Audio Intelligence in their workflows and to quickly build powerful AI tools to give them a competitive edge.

In this article, we’ll break down the Speech AI technology that is having a profound effect in the podcasting industry, as well as the top applications and benefits of incorporating Speech AI into podcast hosting, podcast editing, and podcast monetization platforms.

What is Speech AI?

Speech AI applies AI models to understand speech or spoken data. Speech AI can encompass:

Automatic Speech Recognition (ASR): Automatic speech recognition (ASR) models transcribe and process human speech into readable text. ASR can occur asynchronously, transcribing an audio or video file after it has been recorded, or synchronously with real-time ASR models. ASR models today, like AssemblyAI’s Conformer-2 model, can be trained on enormous amounts of data to achieve superhuman levels of accuracy.
Audio Intelligence: Audio Intelligence is an umbrella term for AI models that enable users to unlock critical information from spoken data. Audio Intelligence models can include Summarization, Content Moderation, Sentiment Analysis, Entity Detection, PII Redaction, and more.
Large Language Models (LLMs): Large Language Models, or LLMs, help users build high-quality Generative AI features on top of voice data.
Frameworks for Large Language Models (LLMs): Frameworks for LLMs let users build these tools and features faster by offering access to LLMs in an easier-to-use package. AssemblyAI’s framework for applying LLMs LeMUR, for example, helps unify a user’s AI stack for audio and build impactful features like generating answers to specific questions, writing custom summaries, generating action items, and more.

What are podcast hosting, editing, and monetization platforms?

Now that we’ve examined what Speech AI is, let’s go over what comprises podcast hosting, editing, and monetization platforms before we more closely analyze the most profound impacts Speech AI can have on each.

Podcast hosting

Podcast hosting platforms store and deliver media files associated with a podcast and are designed to handle the large media file sizes associated with podcast recordings. In addition, podcast hosting platforms often offer additional features such as promotion tools, automated podcast submission, monetization options, statistics and analytics, social sharing features, and more.

Podcast editing

Podcast editing platforms offer a variety of basic and advanced tools to help users get their podcasts ready for publishing. Typically, podcast editing platforms will offer mixing and trimming tools, audio fine-tuning, transcription tools, audio presets, sound removal, intros/outros, sound effects, and more.

Podcast monetization

Podcast monetization is often a feature included in a podcast hosting platform, though there are some standalone services as well.

Automatic advertising tools let users input demographic and ad category information to match podcast episodes to appropriate sponsorships, and help optimize ad slots by finding natural content breaks.

Next, we’ll examine how all three of these categories–hosting, editing, and monetization, can be optimized with Speech AI models.

Speech AI for podcast hosting

Podcast hosting platforms are incorporating several Speech AI models to optimize their tool suites and grow market share.

Today, there are a variety of AI speech transcription models available to choose from, but the key factor to consider is how accurate the model is. Transcription models like Conformer-2 are trained on hours of audio data to achieve near-human level transcription accuracy and significant improvements on proper noun accuracy, alphanumerics, and robustness to noise.

Highly-accurate speech transcription is important for several reasons:

It allows platforms to build tools for podcasters to more easily meet accessibility and compliance requirements by adding subtitles to each podcast episode.
It means that every Generative AI and Audio Intelligence tool built on top of this transcription data will be even more accurate, and useful, to end users.

For example, some podcast hosting platforms are using Generative AI models to build tools that automatically generate show notes. This addition eases use for podcast creators and facilitates a better listener experience.

Podcast hosting platforms are also building tools that automatically detect recurring topics and themes in podcast episodes using Audio Intelligence models such as Topic Detection and Entity Detection. Then, the tool uses this data to more accurately power podcast recommendation engines.

Speech AI for editing

Speech AI has numerous beneficial applications for podcast editing platforms as well.

Generative AI and Audio Intelligence models such as summarization and time stamps (summaries over time) help podcast editing platforms build better tools for transitions and audio clipping, as they help users more easily and quickly locate the areas in each podcast episode to edit.

When applied at a large scale via a framework like LeMUR, Large Language Models can be used to build tools that suggest engaging or high-performing podcast episode titles, auto-generate show notes, suggest follow-up episodes, generate auto-SEO tags, suggest social media posts, and more productivity-augmenting tools that ease a podcast creator’s workflow.

Speech AI for monetization

Speech AI models can also significantly enhance podcast monetization tools and features by helping product teams build intelligent tools that:

Serve relevant ads on podcasts using AI models like Topic Detection to automatically identify common topics and themes across episodes.
Protect brands by automatically detecting sensitive content in transcription data, such as hate speech, references to violence, drugs, or alcohol, and sensitive social issues, with the aid of a Content Moderation model. These are referred to as brand safety tools.

See an example of how a brand safety tool might look below.

Brands can then use these content safety tags to identify and flag potentially harmful content, or content that aligns and/or misaligns with brand values.