Empowering Journalism with AI: Unlocking Podcast Insights
Empowering Journalism with AI: Unlocking Podcast Insights
Modulai collaborated with Svenska Dagbladet to harness AI technology for transcribing and analyzing Swedish podcasts. The partnership enabled journalists to efficiently explore audio content through a user-friendly interface, providing deep insights into topics, entities, emotions, and language intricacies.
Background
The burgeoning podcast industry has made it increasingly challenging to analyze audio content manually. To address this, Svenska Dagbladet partnered with Modulai, leveraging AI tools like OpenAI’s Whisper for speech recognition and pre-trained NLP models for various linguistic analyses. The aim was to provide an efficient way for journalists to gain valuable insights from podcast content.
Solution
The collaboration employed a comprehensive approach that combined advanced speech recognition and NLP models to analyze and search through podcasts. A custom interface was developed, allowing journalists to effortlessly dive into the transformed data. This multi-faceted solution significantly streamlined the process of extracting meaningful information from podcasts.
Tools/Tech
Cutting-edge technologies like OpenAI’s Whisper and pre-trained NLP models were key components of the project. Computational stylistics were also applied to evaluate language complexity. An intuitive search interface was designed to facilitate self-service analysis, making it easier for journalists to delve into the content effectively.
Empowering Journalists at SvD with AI Tools for Podcast Analysis
While podcasts offer a convenient and accessible media format for on-the-go consumption, they can present significant challenges when it comes to searching and analysing content, especially compared to traditional text-based media such as news articles and interviews.
In this blog post, we’ll explore the ways in which AI can be used to analyse podcasts and how AI can empower journalists.
Speech emotion recognition
This post focuses on the work we have done in the field of SER in a project conducted with journalists from SvD, one of Sweden’s top daily newspapers.
The amount of digitally stored speech in the form of interviews, lectures, debates, radio talk show archives, and podcasts is increasingly available. Owing to this, Speech Emotion Recognition (SER) has grown to be one of the most trending research topics in computational linguistics in the last two decades. SER, as the name suggests, refers to the automatic detection of emotion from audio samples.
