Over the past few weeks, I've been exploring solutions to the challenge of audio diarization. Diarization answers the question "who spoke when" in an audio file.
A popular solution in the field is an open-source package that implements various models to perform diarization and achieve high performance with pre-trained pipelines. While this approach performs well in controlled environments, it can face challenges in real-world settings.
When considering diarization for large-scale applications processing significant amounts of speech data, two main requirements emerged:
- Enable near real-time processing of speaker turns for live transcription
- Improve accuracy in global clustering (identifying the correct number of speakers)
The first requirement is relatively straightforward. Processing audio in chunks allows for timely speaker turn detection. However, overlapping speech, common in real-life conversations, presents a significant challenge. One approach to increase accuracy is to treat overlapping speech carefully to avoid introducing errors in global speaker diarization.
The second requirement proves more complex. Many existing solutions use clustering algorithms that can struggle with determining the ideal number of speakers, especially in real-world scenarios where this number is unknown. This can lead to either over- or under-identification of speakers.
To address these challenges, exploring alternative clustering methods that handle high-dimensional data more effectively could yield improvements. By refining the approach to global diarization, it's possible to achieve better results across a variety of meeting types.
Key learnings from this exploration include:
- The choice of pre-trained model for speaker embeddings significantly impacts overall performance.
- While local diarization is well-understood, overlapping speech remains a primary source of errors.
- Global clustering with an unknown number of speakers remains a challenging problem for traditional methods.
These insights highlight the ongoing challenges and areas for potential innovation in the field of audio diarization.