Using a combination of state-of-the art ASR technologies, proprietary Text Segmentation algorithms and advanced NLP analysis, AudioBurst is able to convert radio-based spoken audio into meaningful bursts of transcribed, categorized, enhanced and searchable audio.

Machine Learning is utilized in almost every aspect of AudioBurst's backbone, as huge amounts of data is processed by AudioBurst's servers on a daily basis.

This data is analyzed and used to automatically improve the ASR models, better understand trending discussions to adjust the segmentation algorithms, analyze user's behavior to recommend bursts based on a user's interests, and predict trending topics based on past events.

Additional development and improvement of machine learning procedures is a core element of Audioburst's long term goals.

AudioBurst utilizes state-of-the art ASR technologies which provide highly accurate live speech-to-text analysis.

Parallel computing over unique GPU servers provide the means to achieve this impressive goal. Machine and human-based training data is also processed using GPU servers allowing AudioBurst to educate its models within few hours to ensure accurate interpretation and processing of domestic and international events.
AudioBurst has developed unique Text Segmentation algorithms that combine both text and audio analysis along with machine learning to segment spoken audio.

By analyzing specific trends based on various categories, AudioBurst adjusts its segmentation to ensure segments contain coherent and updated data. AudioBurst continues to develop and improve these algorithms as they serve as the basic core of AudioBurst's unique IP and offering.
Natural language processing is utilized in almost every aspect of AudioBurst automated procedures. Each burst is wrapped with NLP metadata to improve searchability, and adaptation to users interest. AudioBurst offers all this rich data via it's APIs for developers to ease the process of consuming the huge amounts of data running through AudioBurst's servers.