Speech intelligence combines transcription with speaker organization, topic segmentation and content analysis. It prepares calls and meetings as structured information that can be retrieved and used in enterprise knowledge applications.
Make conversation records easier to use
A transcript can contain long discussions, ambiguous wording and personal information. Teams also need to know which speaker turn contains a decision or an action item. Organizing this context supports retrieval and review without assuming that transcription or interpretation is error-free.
The approach
The processing flow brings transcription and contextual analysis together. Speaker diarization separates voices, topic segmentation groups related statements, and summarization identifies relevant content. These steps support downstream applications; speaker labels do not by themselves establish personal identity.
- Transcribe and refine: Use conversation context to help interpret ambiguous wording while retaining the original transcript in AIKNOW Voice.
- Organize speaker turns and topics: Divide conversations into useful retrieval units.
- Extract key content: Summarize discussions and identify decisions, actions, owners and dates. Distinguish explicit statements from AI inferences.
- Mask configured text fields: Detect and mask selected personal information in transcripts; source audio is unchanged.
Business value
- Search conversations
Retrieve relevant discussions by topic and available speaker or time information.
- Prepare working records
Use summaries and action-item extraction to support meeting records and call analysis.
- Review service interactions
Identify recurring inquiries and potential handling issues for further assessment.
Application considerations
- AIKNOW Voice supports Japanese and English, including real-time processing. Recording quality, overlapping speech and vocabulary affect results.
- Results are used automatically. Evaluate transcription, inferred content and text masking using representative samples. Masking may miss information and does not guarantee anonymization.
- Emotion estimation is a broader research application, not a claim about confirmed AIKNOW Voice features. Such estimates are uncertain signals, not reliable statements about a person’s feelings or intent.
Related products
- AIKNOW Voice
Processes speech into structured content with context-aware transcription and configurable text masking.
- AIKNOW Answer
Uses permitted interaction records to propose knowledge improvements for review and testing before release.
References
- Radford, A. et al. (2022). Robust Speech Recognition via Large-Scale Weak Supervision (Whisper). arXiv:2212.04356. https://arxiv.org/abs/2212.04356
- Park, T. J. et al. (2022). A Review of Speaker Diarization: Recent Advances with Deep Learning. Computer Speech & Language, 72. arXiv:2101.09624. https://arxiv.org/abs/2101.09624
- Wu, C. et al. (2025). Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects. Findings of EMNLP 2025. arXiv:2505.20511. https://arxiv.org/abs/2505.20511