Search Results for author: Jan Trmal

Found 13 papers, 3 papers with code

GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio

2 code implementations13 Jun 2021 Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan Trmal, Junbo Zhang, Mingjie Jin, Sanjeev Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, Zhiyong Yan

This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10, 000 hours of high quality labeled audio suitable for supervised training, and 40, 000 hours of total audio suitable for semi-supervised and unsupervised training.

Sentence speech-recognition +1

Multi-task self-supervised learning for Robust Speech Recognition

1 code implementation25 Jan 2020 Mirco Ravanelli, Jianyuan Zhong, Santiago Pascual, Pawel Swietojanski, Joao Monteiro, Jan Trmal, Yoshua Bengio

We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks.

Robust Speech Recognition Self-Supervised Learning +1

Induced Inflection-Set Keyword Search in Speech

1 code implementation WS 2020 Oliver Adams, Matthew Wiesner, Jan Trmal, Garrett Nicolai, David Yarowsky

We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants.

The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines

no code implementations28 Mar 2018 Jon Barker, Shinji Watanabe, Emmanuel Vincent, Jan Trmal

The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

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

no code implementations23 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 Automatic Speech Recognition (ASR) +2

Topic Identification for Speech without ASR

no code implementations22 Mar 2017 Chunxi Liu, Jan Trmal, Matthew Wiesner, Craig Harman, Sanjeev Khudanpur

Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Using of heterogeneous corpora for training of an ASR system

no code implementations1 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 Recognition +2

Low-Resource Contextual Topic Identification on Speech

no code implementations17 Jul 2018 Chunxi Liu, Matthew Wiesner, Shinji Watanabe, Craig Harman, Jan Trmal, Najim Dehak, Sanjeev Khudanpur

In topic identification (topic ID) on real-world unstructured audio, an audio instance of variable topic shifts is first broken into sequential segments, and each segment is independently classified.

General Classification Topic Classification +1

New release of Mixer-6: Improved validity for phonetic study of speaker variation and identification

no code implementations LREC 2016 Eleanor Chodroff, Matthew Maciejewski, Jan Trmal, Sanjeev Khudanpur, John Godfrey

The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies.

Speaker Recognition

DiPCo -- Dinner Party Corpus

no code implementations30 Sep 2019 Maarten Van Segbroeck, Ahmed Zaid, Ksenia Kutsenko, Cirenia Huerta, Tinh Nguyen, Xuewen Luo, Björn Hoffmeister, Jan Trmal, Maurizio Omologo, Roland Maas

We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment.

Benchmarking

Adversarial Attacks and Defenses for Speech Recognition Systems

no code implementations31 Mar 2021 Piotr Żelasko, Sonal Joshi, Yiwen Shao, Jesus Villalba, Jan Trmal, Najim Dehak, Sanjeev Khudanpur

We investigate two threat models: a denial-of-service scenario where fast gradient-sign method (FGSM) or weak projected gradient descent (PGD) attacks are used to degrade the model's word error rate (WER); and a targeted scenario where a more potent imperceptible attack forces the system to recognize a specific phrase.

Adversarial Robustness Automatic Speech Recognition +2

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