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Joint Speech Recognition and Speaker Diarization via Sequence Transduction
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Speech applications dealing with conversations require not only recognizing the spoken words, but also determining who spoke when. The task of assigning words to speakers is typically addressed by merging the outputs of two separate systems, namely, an automatic speech recognition (ASR) system and a speaker diarization (SD) system. The two systems are trained independently with different objective functions. Often the SD systems operate directly on the acoustics and are not constrained to respect word boundaries and this deficiency is overcome in an ad hoc manner. Motivated by recent advances in sequence to sequence learning, we propose a novel approach to tackle the two tasks by a joint ASR and SD system using a recurrent neural network transducer. Our approach utilizes both linguistic and acoustic cues to infer speaker roles, as opposed to typical SD systems, which only use acoustic cues. We evaluated the performance of our approach on a large corpus of medical conversations between physicians and patients. Compared to a competitive conventional baseline, our approach improves word-level diarization error rate from 15.8% to 2.2%.
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“The position of the recording device also has a strong impact on the captured audio.18,20”
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Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker RecordingsPreprint
Chang, X., Kanda, N., Gaur, Y., Wang, X., Meng, Z., & Yoshioka, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.01853 · Cited by 1
End-to-End Speaker-Attributed ASR with TransformerPreprint
Kanda, N., Ye, G., Gaur, Y., Wang, X., Meng, Z., Chen, Z., & Yoshioka, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02128 · Cited by 1
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Dicow: Diarization-Conditioned Whisper for Target Speaker Automatic Speech RecognitionPreprint
Polok, A., Klement, D., Kocour, M., Han, J., Landini, F., Yusuf, B., Wiesner, M., Khudanpur, S. P., Černocký, J., & Burget, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5123534 · Cited by 1
Informed Speech Self-supervised Representation Learning
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Streaming Multi-talker Speech Recognition with Joint Speaker IdentificationPreprint
Lu, L., Kanda, N., Li, J., & Gong, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02109
Speaker diarization assisted ASR for multi-speaker conversationsPreprint
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Masumura, R., Okamura, D., Makishima, N., Ihori, M., Takashima, A., Tanaka, T., & Orihashi, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.01549
Improving End-to-end Models for Set Prediction in Spoken Language Understanding
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Towards lifelong human assisted speaker diarization
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Speaker Diarization for Unlimited Number of Speakers Using Dynamic Linear
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SEAL: Speaker Error Correction using Acoustic-conditioned Large Language Models
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On the Limitations of Speaker Diarization
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TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation
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Whisper with Benefits: A Unified Approach to Speech and Speaker Attribute Recognition
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Speaker Attributed Automatic Speech Recognition Using Speech Aware Llms
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Metadata
- Authors
- Shafey, L. E., Soltau, H., & Shafran, I.
- Affiliations
- Google (United States) (US)
- Venue
- Interspeech 2019
- Publisher
- ISCA
- Published
- 2019-09-15
- Pages
- 396-400
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2957946919
- Cited by
- 104
- References
- 30
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 14 · 2023: 5 · 2022: 21 · 2021: 29 · 2020: 16 · 2019: 2
- FWCI
- 6.50
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speaker diarization, speech recognition, RNN-T, medical conversation, physician-patient interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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