Speaker Diarization With Block Embeddings for Episodic Audio
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Solution Overview
Problem
Existing speaker diarization technologies struggle with efficiently partitioning audio streams containing speech from multiple individuals without requiring speaker fingerprints or prior knowledge of the number of speakers, and they are inadequate for handling overlapping speech.
Innovation Solution
A method involving spatial conversion, block-based embeddings extraction, multi-head attention architecture, embeddings optimization, and double clustering to enhance speaker diarization, allowing for accurate separation of speakers in diverse audio environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speaker diarization is performed on entire audio streams without blocking, then speaker identification accuracy is maintained, but memory consumption and processing load become unmanageable
Solution Approach 1:
The audio stream is divided into fixed-size blocks that can be processed independently. Each block contains a segment of the audio stream that is diarized separately, allowing memory usage to be bounded while maintaining accuracy through proper block design and overlapping regions
2Stability of the object's composition
If speaker diarization is performed on entire audio streams without blocking, then continuous speaker tracking is improved, but processing time and computational load increase significantly
Solution Approach 1:
Processing is segmented into fixed-size blocks that can be handled independently and in parallel. This reduces processing time by allowing efficient block-wise computation while maintaining continuous speaker tracking through block chaining and overlap handling
Solution Approach 2:
Blocks are pre-processed and prepared in advance, with speaker embeddings extracted and stored for later clustering. This preliminary action allows the main diarization logic to run more efficiently on pre-prepared data
3Measurement precision
If embeddings are extracted for all segments before clustering, then comprehensive speaker analysis is achieved, but memory requirements become prohibitive
Solution Approach 1:
The audio stream is divided into fixed-size blocks that can be processed independently. Each block contains a segment of the audio stream that is diarized separately, allowing memory usage to be bounded while maintaining accuracy through proper block design and overlapping regions
Solution Approach 2:
Blocks are pre-processed and prepared in advance, with speaker embeddings extracted and stored for later clustering. This preliminary action allows the main diarization logic to run more efficiently on pre-prepared data
Data Source
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AI summary
Embodiments are disclosed for speaker diarization supporting episodical content. In an embodiment, a method comprises: receiving media data including one or more utterances; dividing the media data into a plurality of blocks; identifying segments of each block of the plurality of blocks associated with a single speaker; extracting embeddings for the identified segments in accordance with a machine learning model, wherein extracting embeddings for identified segments further comprises statistically combining extracted embeddings for identified segments that correspond to a respective continuous utterance associated with a single speaker; clustering the embeddings for the identified segments into clusters; and assigning a speaker label to each of the embeddings for the identified segments in accordance with a result of the clustering. In some embodiments, a voiceprint is used to identify a speaker and the speaker identity for a speaker label.