In year May 2020, comedian and podcaster Joe Rogan announced he had signed a multi-year licensing contract with Spotify, worth approximately $100 million for his podcast.
This shed light on the fact that Podcast finally had come of age. Though its origin can be traced back to the 1980s, witnessing an exceptional rise in popularity with the roll-out of the iPod in the early 00s. The term podcast is, indeed, a portmanteau of iPod and Broadcast.
Aside from videos, podcasts were the second-fastest-growing medium of “the 10’s”. The latest research shows that there are over 47 million episodes available and the number is growing exponentially. Precisely, podcasts include a series of episodes of spoken audio files that can be easily downloaded to a mobile, tablet, or computer for comfortable listening.
Who Listens to Podcasts?
Podcast producers and marketers are always looking for ways to reach unique and targeted audiences. Often, marketers are in search of media environments that people find interactive, while podcast producers are looking to attract audiences that grab attention with trending topics and quality material.
Besides, there are few brands available that have truly hopped on the podcast train and are riding it successfully like Johnson & Johnson, McDonald’s, GE, and Shopify. Here are some podcast stats pocketed from various sources:
- 32% of Americans listen to podcasts at least every month.
- 16 million Americans are podcast fans
- 90% of podcast listeners prefer to listen to a podcast at their home
- 39% of smart-speaker owners listen to podcasts at least once per week.
Put in all, podcasts reach young and affluent audiences who are engaged in interactive and longer content.
The Basic Types of Podcast Formats
There are more than 1-million active podcasts and more than 30 million podcast episodes available globally in over 100 languages. And, the podcast content varies in different formats, subject matter, topics, and themes,
You can easily find podcasts on almost any subject, from emerging technologies like Artificial Intelligence to crime to erotica. This behemoth amount of variety is one of the biggest driving factors in successfully attracting different audiences and micro-audiences.
Some conventional podcasts formats are as follows:
- The interview podcasts format -Startup Podcast
- The Monologue Podcast format -Slate’s The Gist
- Conversational Podcast format – Errthang Show!
- The Panel Podcast Format -The Bean cast
- Storytelling Podcast Format -This American Life
- Repurposed Content-Format -TED Radio Hour
- Theater Podcast Format -Welcome To NighVale
Nowadays, the most popular podcast themes are Business, Health, Comedy, Society and Culture, and News & Politics. And, with such voluminous podcast audio, spanning across an infinite number of subject categories, the job of quickly and precisely retrieving specific content from podcasts sounds daunting at first.
That’s where semantic search comes into the picture!
Semantic Search for Audio & Podcasts
Integrating Semantic Search for Audio makes your podcast files easy to search by semantically indexing the content. Besides, users can also search your complete audio catalog for the same content they want without any manual tagging from your end.
Importantly, semantic search for audio offers service and marketing teams, podcast producers, and heads of sales the power to search audio files through topics, themes, and entities. Also, semantic search automatically annotates podcast data with semantic analysis information without any training requirements. Simple, plug it in and get to work!
With semantic search, it’s easy to find anything with a touch. So, it’s high time to abide goodbye to hours spent listening to audio files and have access to whatever you need at your fingertips.
How Does Semantic Search Work For Podcasts?
Semantic search for audio extracts semantic insights from the podcast content with the multi-phase approach:
- Firstly, it turns audio to text through the use of speech-to-text models. These models are unique to each language and are also developed using neural networks. This eventually brings a transcript of the speech with the timestamps for each word.
- Then, the transcript text is indexed in the Semantic Search for Audio sections. Audio can be broken into smaller sections based on longer pauses or changes in the speaker. By analyzing every section, you have enough context to correctly identify entities, but not much ample content as to lose the ability to associate entities with granular enough timestamps.
- Further, you have timestamps of podcasts that are audio associated with entities, themes, and topics found in the text, with related metadata. So, performing semantic search will bring matching sections, which in return relate to snippets in the podcast’s source audio.
Finally
Podcast analysis highlights the entities found at each timestamp, allowing users to scrub to the exact point they have been looking for the content. After all, enabling meaningful search is not easy, but the impact can be everlasting if done well.
