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
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 currently 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 Speech-to-Text accuracy and affordability is not just a marketing claim, it's proven by an open-source benchmark. You can evaluate Leopard Speech-to-Text's accuracy by creating a Picovoice Console account.
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