Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings

no code implementations • 7 Oct 2021 • Jialu Li, Vimal Manohar, Pooja Chitkara, Andros Tjandra, Michael Picheny, Frank Zhang, Xiaohui Zhang, Yatharth Saraf

Domain-adversarial training (DAT) and multi-task learning (MTL) are two common approaches for building accent-robust ASR models.

Automatic Speech Recognition Multi-Task Learning

On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models

no code implementations • 9 Jul 2021 • Xiaohui Zhang, Vimal Manohar, David Zhang, Frank Zhang, Yangyang Shi, Nayan Singhal, Julian Chan, Fuchun Peng, Yatharth Saraf, Mike Seltzer

Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria.

Automatic Speech Recognition

Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition

no code implementations • 14 Jun 2021 • Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed

In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR).

Frame Speech Recognition

Large scale weakly and semi-supervised learning for low-resource video ASR

no code implementations • 16 May 2020 • Kritika Singh, Vimal Manohar, Alex Xiao, Sergey Edunov, Ross Girshick, Vitaliy Liptchinsky, Christian Fuegen, Yatharth Saraf, Geoffrey Zweig, Abdel-rahman Mohamed

Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems.

Frame Speech Recognition

CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings

no code implementations • 20 Apr 2020 • Shinji Watanabe, Michael Mandel, Jon Barker, Emmanuel Vincent, Ashish Arora, Xuankai Chang, Sanjeev Khudanpur, Vimal Manohar, Daniel Povey, Desh Raj, David Snyder, Aswin Shanmugam Subramanian, Jan Trmal, Bar Ben Yair, Christoph Boeddeker, Zhaoheng Ni, Yusuke Fujita, Shota Horiguchi, Naoyuki Kanda, Takuya Yoshioka, Neville Ryant

Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).

Speaker Diarization Speech Enhancement +2

Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages

no code implementations • 23 Feb 2018 • Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, Jan Trmal, Zhongqiang Huang, Najim Dehak, Sanjeev Khudanpur

Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search.

Automatic Speech Recognition Humanitarian

Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework

no code implementations • 12 Jun 2017 • Xiaohui Zhang, Vimal Manohar, Daniel Povey, Sanjeev Khudanpur

Speech recognition systems for irregularly-spelled languages like English normally require hand-written pronunciations.

Speech Recognition

Using of heterogeneous corpora for training of an ASR system

no code implementations • 1 Jun 2017 • Jan Trmal, Gaurav Kumar, Vimal Manohar, Sanjeev Khudanpur, Matt Post, Paul McNamee

The paper summarizes the development of the LVCSR system built as a part of the Pashto speech-translation system at the SCALE (Summer Camp for Applied Language Exploration) 2015 workshop on "Speech-to-text-translation for low-resource languages".

Speech Recognition Speech-to-Text Translation +1

Purely sequence-trained neural networks for ASR based on lattice-free MMI

no code implementations • INTERSPEECH 2016 2016 • Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahrmani, Vimal Manohar, Xingyu Na, Yiming Wang, Sanjeev Khudanpur

Models trained with LFMMI provide a relative word error rate reduction of ∼11. 5%, over those trained with cross-entropy objective function, and ∼8%, over those trained with cross-entropy and sMBR objective functions.

Frame Speech Recognition