CloudASR is a cloud based automatic speech recognition platform which supports both batch and online speech recognition. The key features are scalability, customizability and easy deployment. It is tested to be able to handle more than 1000 parallel requests given enough computational resources.
MT–ComparEval is a tool for comparison and evaluation of machine translation. Translations can be compared according to automatic metrics for whole documents or single sentences. Differences between translations can be visualized by highlighting matching/missing n-grams etc.
O. Klejch, E. Wallington, P.Bell "Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR." In Interspeech 2022.
T. Reitmaier, E. Wallington, D. K. Raju, O. Klejch, J. Pearson, M. Jones, P. Bell, and S. Robinson "Opportunities and Challenges of Automatic Speech Recognition Systems for Low-Resource Language Speakers." In CHI 2022.
E. Wallington, B. Kershenbaum, O. Klejch, P. Bell "On the learning dynamics of semi-supervised training for ASR." In Interspeech 2021.
O. Klejch, E. Wallington, P. Bell "The CSTR System for Multilingual and Code-Switching ASR Challenges for Low Resource Indian Languages." In Interspeech 2021.
P. Bell, J. Fainberg, O. Klejch, J. Li, S. Renals, P. Swietojanski "Adaptation algorithms for neural network-based speech recognition: An overview." In IEEE Open Journal of Signal Processing, 2020.
J. Roth, S. Chaudhuri, O. Klejch, R. Marvin, A. Gallagher, L. Kaver, S. Ramaswamy, A. Stopczynski, C. Schmid, Z. Xi, C. Pantofaru "Ava active speaker: An audio-visual dataset for active speaker detection." In ICASSP 2020.
O. Klejch, J. Fainberg, P. Bell, S. Renals "Speaker adaptive training using model agnostic meta-learning." In ASRU 2019.
J. Fainberg, O. Klejch, E. Loweimi, P. Bell, S. Renals "Acoustic model adaptation from raw waveforms with SincNet." In ASRU 2019.
J. Fainberg, O. Klejch, P. Bell, S. Renals "Lattice-based lightly-supervised acoustic model training." In Interspeech 2019.
O. Klejch, J. Fainberg, P. Bell, S. Renals "Lattice-based unsupervised test-time adaptation of neural network acoustic models" arxiv:1906.11521, 2019.
O. Klejch, J. Fainberg, P. Bell. "Learning to adapt: a meta-learning approach for speaker adaptation." In Interspeech 2018.
E. Tsunoo, O. Klejch, P. Bell, S. Renals. "Hierarchical recurrent neural network for story segmentation using fusion of lexical and acoustic features." In ASRU 2017.
O. Klejch, P. Bell, and S. Renals. "Sequence-to-sequence models for punctuated transcription combining lexical and acoustic features." In ICASSP 2017.
O. Klejch, P. Bell, and S. Renals. "Punctuated transcription of multi-genre broadcasts using acoustic and lexical approaches." In SLT 2016.
R. Sudarikov, M. Popel, O. Bojar, A. Burchardt, O. Klejch. "Using MT-ComparEval." In LREC 2016 Workshop: Translation Evaluation 2016.
N. Aranberri, E. Avramidis, A. Burchardt, O. Klejch, M. Popel and M. Popović. "Tools and Guidelines for Principled Machine Translation Development." In LREC 2016.
O. Klejch, O. Platek, L. Zilka, and F. Jurcicek. "CloudASR: Platform and Service." In Text, Speech, and Dialogue, pp. 334-341. Springer International Publishing, 2015.
O. Klejch, E. Avramidis, A. Burchardt, and M. Popel. "MT-ComparEval: Graphical evaluation interface for Machine Translation development." The Prague Bulletin of Mathematical Linguistics 104, no. 1 (2015): 63-74.
O. Klejch, O. Platek, L. Zilka, and F. Jurcicek. "CloudASR: Platform and Service." Demo in SLT 2014.