Pushing the Boundaries of Speech AI: Solving High-Fidelity Multi-Speaker Separation
Speaker separation has long been a cornerstone problem in speech processing, with applications spanning transcription, diarization, and conversational AI.
From a machine point of view, the easiest conversations to track are ones in which a single speaker speaks for a finite duration, followed by the next speaker. Real-world conversation, however, is rarely so tidy. Speakers enter and exit dynamically, often interrupting or overlapping in unpredictable ways. This challenge has led researchers to explore continuous speaker separation (CSS), an approach designed to handle arbitrary numbers of speakers over extended time frames. This work has traditionally focused on low-fidelity audio in 8kHz or 16kHz sample rate, like that produced for meeting transcripts and in call centers. However, most research has ignored the need for high-fidelity separation, like the kind of audio produced in films, TV, podcasts, or in many AI content workflows.
At AudioShake, we refer to this publicly as multi-speaker separation—a technology designed to isolate multiple speakers in high fidelity. High-fidelity multi-speaker separation faces many of the same challenges as with low-fidelity audio, as well as introduces new challenges.
The Challenges of Multi-Speaker Separation
Separating individual speakers from low-or high-fidelity audio present a range of research challenges:
- Speaker Detection, Transitions and Overlapping Speech
Multi-speaker separation models must both determine when speakers enter and exit a conversation, as well as who is speaking when--a process called "diarization." Many factors can throw off the accuracy of diarization, including the similarity of the voices, or the presence of background noise that may obscure one or multiple voices.
- Dataset Limitations and Evaluation Complexity
Datasets for traditional speaker separation often assume clean, finite mixtures. Multi-speaker separation requires datasets that reflect realistic, dynamic, and emotional speaker behavior—something historically difficult to collect at scale. As a result, research in this space often relies on simulated conversations or constrained real-world recordings. This is an even bigger challenge with high-fidelity separation, in that many content workflows--such as film--have evolved over the years to explicitly avoid speaker overlap, precisely because it introduces so many editing headaches in post-production.
- Machine-friendly vs. Human-Friendly
Continuous speaker separation for low-fidelity use cases like transcription are under tight latency constraints—for example, being able to transcribe a meeting conversation as it happens. It doesn’t matter how the output “sounds” to the human ear, because no human will ever hear that output. In contrast, high-resolution use cases tend to be less time sensitive but have a very high quality bar. For these high-quality use cases, grainy or artifact-heavy speaker output can render an entire workflow useless. In an ideal world, the output from a high-fidelity speaker separation model should sound as good as if each individual had been recorded in an isolated recording studio.
Introducing AudioShake’s High-Fidelity Multi-Speaker Separation
At AudioShake, we are excited to announce the launch of our high-resolution multi-speaker separation technology —the first of its kind to offer true hi-fi continuous speaker separation. Our model operates at a high sampling rate, making it suitable for broadcast-quality audio. It delivers clean dialogue tracks across a wide variety of speech content, from media production and podcasting to accessibility services.
Traditional audio tools struggle to separate overlapping speech, but AudioShake’s multi-speaker separation technology can detect, diarize, and isolate speech into distinct streams, even for hours-long recordings. Our advanced neural architecture ensures superior speaker tracking across different overlap scenarios, making it ideal for use in film, TV, podcasting, voice AI, and other post-production workflows. This technology represents a major leap forward in speech AI, enabling cleaner, more adaptable, and more precise voice separation than ever before.
Bringing Multi-Speaker Separation to Real-World Applications
Enterprise users can access Multi-Speaker Separation via AudioShake Live, as well as via AudioShake’s API.
We’re excited to continue pushing the frontiers of audio AI and invite companies, developers, and creators to explore the possibilities of multi-speaker separation with us. Read our documentation or get in touch to get started.