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Vimal Manohar
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2020 – today
- 2024
- [c29]Ruizhe Huang, Xiaohui Zhang, Zhaoheng Ni, Li Sun
, Moto Hira, Jeff Hwang, Vimal Manohar, Vineel Pratap, Matthew Wiesner, Shinji Watanabe, Daniel Povey, Sanjeev Khudanpur:
Less Peaky and More Accurate CTC Forced Alignment by Label Priors. ICASSP 2024: 11831-11835 - [i10]Ruizhe Huang, Xiaohui Zhang, Zhaoheng Ni, Li Sun
Less Peaky and More Accurate CTC Forced Alignment by Label Priors. CoRR abs/2406.02560 (2024) - 2023
- [c28]Tejas Jayashankar, Jilong Wu, Leda Sari, David Kant, Vimal Manohar, Qing He:
Self-Supervised Representations for Singing Voice Conversion. ICASSP 2023: 1-5 - [c27]Mumin Jin, Prashant Serai, Jilong Wu, Andros Tjandra, Vimal Manohar, Qing He:
Voice-Preserving Zero-Shot Multiple Accent Conversion. ICASSP 2023: 1-5 - [c26]Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, Wei-Ning Hsu:
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale. NeurIPS 2023 - [i9]Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, Wei-Ning Hsu:
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale. CoRR abs/2306.15687 (2023) - 2022
- [i8]Jason Fong, Yun Wang, Prabhav Agrawal, Vimal Manohar, Jilong Wu, Thilo Köhler, Qing He:
Towards zero-shot Text-based voice editing using acoustic context conditioning, utterance embeddings, and reference encoders. CoRR abs/2210.16045 (2022) - [i7]Mumin Jin, Prashant Serai, Jilong Wu, Andros Tjandra, Vimal Manohar, Qing He:
Voice-preserving Zero-shot Multiple Accent Conversion. CoRR abs/2211.13282 (2022) - 2021
- [c25]Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed:
Kaizen: Continuously Improving Teacher Using Exponential Moving Average for Semi-Supervised Speech Recognition. ASRU 2021: 518-525 - [c24]Xiaohui Zhang, Vimal Manohar, David Zhang, Frank Zhang, Yangyang Shi, Nayan Singhal, Julian Chan, Fuchun Peng, Yatharth Saraf, Mike Seltzer:
On Lattice-Free Boosted MMI Training of HMM and CTC-Based Full-Context ASR Models. ASRU 2021: 1026-1033 - [i6]Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed:
Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition. CoRR abs/2106.07759 (2021) - [i5]Xiaohui Zhang, Vimal Manohar, David Zhang, Frank Zhang, Yangyang Shi, Nayan Singhal, Julian Chan, Fuchun Peng, Yatharth Saraf, Mike Seltzer:
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models. CoRR abs/2107.04154 (2021) - 2020
- [c23]Kritika Singh, Vimal Manohar, Alex Xiao, Sergey Edunov, Ross B. Girshick, Vitaliy Liptchinsky, Christian Fuegen, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed:
Large Scale Weakly and Semi-Supervised Learning for Low-Resource Video ASR. INTERSPEECH 2020: 3770-3774 - [i4]Kritika Singh, Vimal Manohar, Alex Xiao, Sergey Edunov, Ross B. Girshick, Vitaliy Liptchinsky, Christian Fuegen, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed:
Large scale weakly and semi-supervised learning for low-resource video ASR. CoRR abs/2005.07850 (2020)
2010 – 2019
- 2019
- [c22]Jinyi Yang, Lucas Ondel, Vimal Manohar, Hynek Hermansky
Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings. ICASSP 2019: 3747-3751 - [c21]Vimal Manohar, Szu-Jui Chen, Zhiqi Wang, Yusuke Fujita, Shinji Watanabe
Acoustic Modeling for Overlapping Speech Recognition: Jhu Chime-5 Challenge System. ICASSP 2019: 6665-6669 - [c20]Ashish Arora, Paola García, Shinji Watanabe
Using ASR Methods for OCR. ICDAR 2019: 663-668 - [c19]Yiming Wang, David Snyder, Hainan Xu, Vimal Manohar, Phani Sankar Nidadavolu, Daniel Povey, Sanjeev Khudanpur:
The JHU ASR System for VOiCES from a Distance Challenge 2019. INTERSPEECH 2019: 2488-2492 - 2018
- [c18]Vimal Manohar, Hossein Hadian
Semi-Supervised Training of Acoustic Models Using Lattice-Free MMI. ICASSP 2018: 4844-4848 - [c17]Matthew Maciejewski, David Snyder, Vimal Manohar, Najim Dehak
Characterizing Performance of Speaker Diarization Systems on Far-Field Speech Using Standard Methods. ICASSP 2018: 5244-5248 - [c16]Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, Jan Trmal, Zhongqiang Huang, Najim Dehak
Automatic Speech Recognition and Topic Identification from Speech for Almost-Zero-Resource Languages. INTERSPEECH 2018: 2052-2056 - [c15]Gregory Sell, David Snyder, Alan McCree, Daniel Garcia-Romero, Jesús Villalba
Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge. INTERSPEECH 2018: 2808-2812 - [c14]Vimal Manohar, Pegah Ghahremani, Daniel Povey, Sanjeev Khudanpur:
A Teacher-Student Learning Approach for Unsupervised Domain Adaptation of Sequence-Trained ASR Models. SLT 2018: 250-257 - [i3]Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, Jan Trmal, Zhongqiang Huang, Sanjeev Khudanpur, Najim Dehak:
The JHU Speech LOREHLT 2017 System: Cross-Language Transfer for Situation-Frame Detection. CoRR abs/1802.08731 (2018) - 2017
- [j1]Mark A. Hasegawa-Johnson
ASR for Under-Resourced Languages From Probabilistic Transcription. IEEE ACM Trans. Audio Speech Lang. Process. 25(1): 46-59 (2017) - [c13]Pegah Ghahremani, Vimal Manohar, Hossein Hadian
Investigation of transfer learning for ASR using LF-MMI trained neural networks. ASRU 2017: 279-286 - [c12]Vimal Manohar, Daniel Povey, Sanjeev Khudanpur:
JHU Kaldi system for Arabic MGB-3 ASR challenge using diarization, audio-transcript alignment and transfer learning. ASRU 2017: 346-352 - [c11]Gaofeng Cheng, Vijayaditya Peddinti, Daniel Povey, Vimal Manohar, Sanjeev Khudanpur, Yonghong Yan:
An Exploration of Dropout with LSTMs. INTERSPEECH 2017: 1586-1590 - [c10]Xiaohui Zhang, Vimal Manohar, Daniel Povey, Sanjeev Khudanpur:
Acoustic Data-Driven Lexicon Learning Based on a Greedy Pronunciation Selection Framework. INTERSPEECH 2017: 2541-2545 - [c9]Jan Trmal, Matthew Wiesner, Vijayaditya Peddinti, Xiaohui Zhang, Pegah Ghahremani, Yiming Wang, Vimal Manohar, Hainan Xu, Daniel Povey, Sanjeev Khudanpur:
The Kaldi OpenKWS System: Improving Low Resource Keyword Search. INTERSPEECH 2017: 3597-3601 - [i2]Jan Trmal, Gaurav Kumar, Vimal Manohar, Sanjeev Khudanpur, Matt Post, Paul McNamee:
Using of heterogeneous corpora for training of an ASR system. CoRR abs/1706.00321 (2017) - [i1]Xiaohui Zhang, Vimal Manohar, Daniel Povey, Sanjeev Khudanpur:
Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework. CoRR abs/1706.03747 (2017) - 2016
- [c8]Chunxi Liu, Preethi Jyothi, Hao Tang, Vimal Manohar, Rose Sloan, Tyler Kekona, Mark Hasegawa-Johnson, Sanjeev Khudanpur:
Adapting ASR for under-resourced languages using mismatched transcriptions. ICASSP 2016: 5840-5844 - [c7]Vijayaditya Peddinti, Vimal Manohar, Yiming Wang, Daniel Povey, Sanjeev Khudanpur:
Far-Field ASR Without Parallel Data. INTERSPEECH 2016: 1996-2000 - [c6]Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahremani, Vimal Manohar, Xingyu Na, Yiming Wang, Sanjeev Khudanpur
Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI. INTERSPEECH 2016: 2751-2755 - [c5]Pegah Ghahremani, Vimal Manohar, Daniel Povey, Sanjeev Khudanpur:
Acoustic Modelling from the Signal Domain Using CNNs. INTERSPEECH 2016: 3434-3438 - 2015
- [c4]Vijayaditya Peddinti, Guoguo Chen, Vimal Manohar, Tom Ko
JHU ASpIRE system: Robust LVCSR with TDNNS, iVector adaptation and RNN-LMS. ASRU 2015: 539-546 - [c3]Vimal Manohar, Daniel Povey, Sanjeev Khudanpur:
Semi-supervised maximum mutual information training of deep neural network acoustic models. INTERSPEECH 2015: 2630-2634 - 2014
- [c2]Jan Trmal, Guoguo Chen, Daniel Povey, Sanjeev Khudanpur, Pegah Ghahremani, Xiaohui Zhang, Vimal Manohar, Chunxi Liu, Aren Jansen, Dietrich Klakow, David Yarowsky, Florian Metze:
A keyword search system using open source software. SLT 2014: 530-535 - 2013
- [c1]Vimal Manohar, Srinivas C. Bhargav, Srinivasan Umesh
Acoustic modeling using transform-based phone-cluster adaptive training. ASRU 2013: 49-54
Coauthor Index
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