Creating Top Hiring Intelligence Platforms with ASR, NLP, and NLU Tools

1. Accurate Transcription: Hiring Intelligence Platforms must use accurate, reliable Speech-to-Text technology to create readable transcripts of interviews. Start-of-the-art Speech Recognition APIs like Automatic Casing and Punctuation models, Paragraph Detection features, and Speaker Diarization APIs increase the readability and accuracy of these transcriptions, making it easier for hiring managers to review candidate responses. 2. Generate Highlights and Key Analysis: In addition to providing an accurate, highly readable transcript, Hiring Intelligence Platforms must generate highlights and key analysis for users. This could include automatically searching transcripts for relevant skills or experience, creating highlight reels of key talking points, or analyzing a candidate’s overall behavior to determine best fit for the available role. Audio Intelligence APIs like Auto Chapters/Text Summarization APIs, Entity Detection APIs, Topic Detection APIs, and Sentiment Analysis APIs can be used to identify and label important information in interview transcripts, helping hiring managers more easily review candidate responses and make smarter hiring decisions. 3. Categorize, Tag, and Search Insights: Finally, Hiring Intelligence Platforms can use Speech Transcription and Audio Intelligence to categorize, tag, and search candidate interviews. For example, platforms could use this data to generate indexable categories that users can then use to tag or search, similar to how users use hashtags on platforms like Twitter. These “auto” or “smart” tags can be used for internal collaboration or for automatically attaching notes to indexed sections of interviews. Smart tags also make searching through and screening responses much faster and more efficient for hiring managers.