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Speaker attribution with voice profiles by graph-based semi-supervised learning
Speaker attribution is required in many real-world applications, such as meeting transcription, where speaker identity is assigned to each utterance according to speaker voice profiles.
Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models
We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach.
Speaker diarization with session-level speaker embedding refinement using graph neural networks
Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a global loss defined on the training data, which could be sub-optimal for distinguishing speakers locally in a specific meeting session.
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