The podcast industry is growing. The growth comes with competition to get listeners’ attention. However, the unstructured nature of audio data hinders publishers’ ability to use it further for analysis to increase engagement or monetization. Publishers now build podcast transcription services and podcast search engines that are accurate, scalable and affordable to analyze and monetize their content.
Cost-effective Audio Transcription with Leopard
The standard and the most known approach to structure voice data is to transform voice data into text data. With the advancements in technology, as the accuracy of AI models improves, most organizations choose machine transcription over human transcription. However, even using machines can be a significant cost item. For example, Google Speech-to-Text Enhanced costs $2.16 per hour. Given the number of shows and episodes, this could easily become a huge cost burden on publishers. Along with other benefits, edge computing provides significant cost-effectiveness over cloud computing. Leopard Speech-to-Text offers cloud-level accuracy on the edge and is 10 to 20 times cheaper than cloud-based speech-to-text solutions.
Leopard’s accuracy and affordability is not just a marketing claim, it’s proven by an open-source benchmark. Anyone can build a free podcast transcription with Picovoice’s Free Tier, even for commercial purposes.
Limitless Audio Search with Octopus
Despite the advancements in voice recognition, there is no 100% accurate speech to text solution on the market. Even human transcribers do not commit to 100% accuracy. Especially when it comes to homophones such as “to, too or too” and “botox or boat ox” or brand and individual names such as “Hermes” and “Khaleesi”, the speech-to-text software has difficulties. Speech indexing is a complementary solution to be used for these unique cases. Picovoice’s Speech-to-Index Engine, Octopus, indexes speech directly without relying on a text representation and makes even a massive amount of voice data searchable and discoverable. Its acoustic-only approach boosts accuracy by removing the out-of-vocabulary limitation.