The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Joint Speech Recognition and Speaker Diarization via Sequence Transduction
Open accessAbstract
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”
From Challenges of developing a digital scribe to reduce clinical documentation burden (2019) · cites this paper as [20]
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Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
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Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
X-Vectors: Robust DNN Embeddings for Speaker Recognition
Snyder, D., Garcia-Romero, D., Sell, G., Povey, D., & Khudanpur, S. P. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461375 · Cited by 2,794
VoxCeleb2: Deep Speaker Recognition
Chung, J. S., Nagrani, A., & Zisserman, A. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-1929 · Cited by 2,256
Sequence Transduction with Recurrent Neural NetworksPreprint
Graves, A. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1211.3711 · Cited by 1,842
Speaker Diarization: A Review of Recent Research
Anguera, X., Bozonnet, S., Evans, N., Fredouille, C., Friedland, G. H., & Vinyals, O. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2125954 · Cited by 704
An overview of automatic speaker diarization systems
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Speaker-attributed meeting transcription refinement with constrained open-weight language models
Deonise, C.-A., Coconu, T.-M., Bajwa, M. K. Z., Negru, C., Mocanu, B.-C., Castiglione, A., & Pop, F. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108648
End-to-End Joint ASR and Speaker Role Diarization With Child-Adult Interactions
Xu, A.-F., Feng, T.-T., Bishop, S., Lord, C., & Narayanan, S. S. (2026) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslpro.2026.3707421 · Cited by 4
Optimizing WhisperX and Pyannote Integration for Emotional Speech Diarization
Sharma, S., Kumar, R., & Gupta, M. (2026) · International Conference on Innovations in Cybersecurity and Data Science (ICICDS) · doi:10.1109/icicds70526.2026.11604919
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
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