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Check out more on the benchmark here
https://t.co/h4AlwiFYIH
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Pulse 在 Voice Arena 的说话人识别与语音转写赛道中,以 24.4% 的说话人识别错误率排名第一;同赛道下一名 ElevenLabs Scribe v2 为 40.7%。这一赛道目前有七个系统,评测使用真实对话的远场录音,并以每位说话人的独立麦克风录音制作参考标签。
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Check out more on the benchmark here
https://t.co/h4AlwiFYIH
Congrats @smallest_AI on taking #1 for diarization + ASR on @voicearena_ai.
Its a very effective real-world kind of benchmark:
> models hear far-field room audio (i.e. microphone is some distance away from the people speaking), but they’re scored against labels built from a mic on every speaker.
> Realistic input, accurate ground truth. That combination is rare.
Many diarization benchmarks either use clean audio or have messy labels.
@voicearena_ai avoids both: models get far-field recordings of real in-person conversations, and the ground truth comes from individual mics on each speaker. #1 really means something.
Congrats @smallest_AI.
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Pulse by @smallest_AI currently ranks #1 in the Diarization + ASR track by DER, as the newest entry on the Voice Arena Diarization Bench.
The Diarization + ASR track ranks Speaker-Attributed ASR systems that return speaker labels together with the transcript. Seven systems are in it today. Pulse scores 24.4% DER at a strict 0 ms collar. The next system, ElevenLabs Scribe v2, scores 40.7%. That is 1.7x lower error than the next best in the track.
Most of the gap is missed speech. For every other system in the track, missed speech is the largest part of the error, above 30% for each of them. Pulse misses 4.4% of reference speech, the lowest in the track.
On the full board of 13 systems, dedicated diarization models and Speaker-Attributed ASR systems together, Pulse places 5th. It is the highest-ranked Speaker-Attributed ASR system on the board and sits ahead of two dedicated diarization models.
The result holds across settings:
In-person recordings : 24.7% DER
In online calls : 22.4% DER
Congratulations to @smallest_AI 👏
View full results, per track and per condition: https://t.co/T9H6hLEUxR
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