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An analysis of euclidean vs. Graph-Based framing for bilingual lexicon induction from word embedding spaces

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arXiv 2021年

作者： Marchisio, Kelly Park, Youngser Saad-Eldin, Ali Alyakin, Anton Duh, Kevin Priebe, Carey Koehn, Philipp Depts. of Computer Science Depts. of Applied Mathematics and Statistics Depts. of Biomedical Engineering Depts. of Center for Imaging Science Depts. of Human Language Technology Center of Excellence Johns Hopkins University United States

Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node’s graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at https://***/kellymarchisio/euc-v-graph-bli. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

Using ASR methods for OCR 15

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Using ASR methods for OCR

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Arora, Ashish Garcia, Paola Watanabe, Shinji Manohar, Vimal Shao, Yiwen Khudanpur, Sanjeev Chang, Chun Chieh Rekabdar, Babak Babaali, Bagher Povey, Daniel Etter, David Raj, Desh Hadian, Hossein Trmal, Jan Center for Language and Speech Processing Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States Department of Computer Engineering Sharif University of Technology Iran School of Mathematics Statistics and Computer Sciences College of Science University of Tehran Iran

ISBN: (纸本)9781728128610

Hybrid deep neural network hidden Markov models (DNN-HMM) have achieved impressive results on large vocabulary continuous speech recognition (LVCSR) tasks. However, the recent approaches using DNN-HMM models are not explored much for text recognition. Inspired by the current work in automatic speech recognition (ASR) and machine translation, we present an open vocabulary sub-word text recognition system. The sub-word lexicon and sub-word language model (LM) helps in overcoming the challenge of recognizing out of vocabulary (OOV) words, and a time delay neural network (TDNN) and convolution neural network (CNN) based DNN-HMM optical model (OM) efficiently models the sequence dependency in the line image. We present results on 12 datasets with training data varying from 6k lines to 600k lines. The system is built for 8 languages, i.e., English, French, Arabic, Chinese, Farsi, Tamil, Russian, and Korean. We report competitive results on several commonly used handwritten and printed text datasets. © 2019 IEEE.

关键词： Hidden Markov models

Zero-Shot Pronunciation Lexicons for Cross-language Acoustic Model Transfer

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Zero-Shot Pronunciation Lexicons for Cross-Language Acoustic...

IEEE Workshop on Automatic speech Recognition and Understanding

作者： Matthew Wiesner Oliver Adams David Yarowsky Jan Trmal Sanjeev Khudanpur Center for Language and Speech Processing The Johns Hopkins University USA Human Language Technology Center of Excellence The Johns Hopkins University USA

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

Existing acoustic models can be transferred to any language with a pronunciation lexicon (lexicon) that uses the same set of sub-word units as in training. Unfortunately such lexicons are not readily available in many low-resource languages. We bypass this requirement and create lexicons by training a grapheme-to-phoneme (G2P) transducer on a subset of words from other languages for which pronunciations are available. The subset of words is selected based on how representative it is of target language text. We find that cross-language acoustic model transfer using our selection strategy outperforms selection based on language similarity, and results in ASR performance approaching that of hand-crafted rule based lexicons in the majority of cases.

关键词： Training Silicon Acoustics Measurement Error analysis Optimization Greedy algorithms

Analysis of Robustness of Deep Single-Channel speech Separation Using Corpora Constructed From Multiple Domains

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Analysis of Robustness of Deep Single-Channel Speech Separat...

IEEE Workshop on Applications of Signal processing to Audio and Acoustics

作者： Matthew Maciejewski Gregory Sell Yusuke Fujita Leibny Paola Garcia-Perera Shinji Watanabe Sanjeev Khudanpur Center for Language and Speech Processing The Johns Hopkins University USA Human Language Technology Center of Excellence The Johns Hopkins University USA

Deep-learning based single-channel speech separation has been studied with great success, though evaluations have typically been limited to relatively controlled environments based on clean, near-field, and read speech. This work investigates the robustness of such representative techniques in more realistic environments with multiple and diverse conditions. To this end, we first construct datasets from the Mixer 6 and CHiME-5 corpora, featuring studio interviews and dinner parties respectively, using a procedure carefully designed to generate desirable synthetic overlap data sufficient for evaluation as well as for training deep learning models. Using these new datasets, we demonstrate the substantial shortcomings in mismatched conditions of these separation techniques. Though multi-condition training greatly mitigated the performance degradation in near-field conditions, one of the important findings is that both matched and multi-condition training have significant gaps from the oracle performance in far-field conditions, which advocates a need for extending existing separation techniques to deal with far-field/highly-reverberant speech mixtures.

关键词： Mixers Training Microphones Robustness speech recognition Conferences Signal processing

Probing the information encoded in x-vectors

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arXiv 2019年

作者： Raj, Desh Snyder, David Povey, Daniel Khudanpur, Sanjeev Center for Language and Speech Processing & Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States

关键词： Embeddings

Spoken language Recognition using X-vectors

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Spoken Language Recognition using X-vectors

2018 Speaker and language Recognition Workshop, ODYSSEY 2018

作者： Snyder, David Garcia-Romero, Daniel McCree, Alan Sell, Gregory Povey, Daniel Khudanpur, Sanjeev Center for Language and Speech Processing Human Language Technology Center of Excellence The Johns Hopkins University United States

In this paper, we apply x-vectors to the task of spoken language recognition. This framework consists of a deep neural network that maps sequences of speech features to fixed-dimensional embeddings, called x-vectors. Long-term language characteristics are captured in the network by a temporal pooling layer that aggregates information across time. Once extracted, x-vectors utilize the same classification technology developed for i-vectors. In the 2017 NIST language recognition evaluation, x-vectors achieved excellent results and outperformed our state-of-the-art i-vector systems. In the post-evaluation analysis presented here, we experiment with several variations of the x-vector framework, and find that the best performing system uses multilingual bottleneck features, data augmentation, and a discriminative Gaussian classifier. © ODYSSEY 2018. All rights reserved.

关键词： Vectors

Zero Resource Speaking Rate Estimation from Change Point Detection of Syllable-like Units 44

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Zero Resource Speaking Rate Estimation from Change Point Det...

44th IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2019

作者： Nayak, Shekhar Bhati, Saurabhchand Rama Murty, K. Sri Department of Electrical Engineering Indian Institute of Technology Hyderabad India Center for Language and Speech Processing Johns Hopkins University United States

ISBN: (纸本)9781479981311

Speaking rate is an important attribute of the speech signal which plays a crucial role in the performance of automatic speech processing systems. In this paper, we propose to estimate the speaking rate by segmenting the speech into syllable-like units using end point detection algorithms which do not require any training and fine-tuning. Also, there are no predefined constraints on the expected number of syllabic segments. The acoustic subword units are obtained only from speech signal to estimate the speaking rate without any requirement of transcriptions or phonetic knowledge of the speech data. A recent theta-rate oscillator based syllabification algorithm is also employed for speaking rate estimation. The performance is evaluated on TIMIT corpus and spontaneous speech from Switchboard corpus. The correlation results are comparable to recent algorithms which are trained with specific training set and/or make use of the available transcriptions. © 2019 IEEE.

关键词： Point contacts

Performance monitoring for end-to-end speech recognition

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arXiv 2019年

作者： Li, Ruizhi Sell, Gregory Hermansky, Hynek Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

Measuring performance of an automatic speech recognition (ASR) system without ground-truth could be beneficial in many scenarios, especially with data from unseen domains, where performance can be highly inconsistent. In conventional ASR systems, several performance monitoring (PM) techniques have been well-developed to monitor performance by looking at triphone posteriors or pre-softmax activations from neural network acoustic modeling. However, strategies for monitoring more recently developed end-to-end ASR systems have not yet been explored, and so that is the focus of this paper. We adapt previous PM measures (Entropy, M-measure and Autoencoder) and apply our proposed RNN predictor in the end-to-end setting. These measures utilize the decoder output layer and attention probability vectors, and their predictive power is measured with simple linear models. Our findings suggest that decoder-level features are more feasible and informative than attention-level probabilities for PM measures, and that M-measure on the decoder posteriors achieves the best overall predictive performance with an average prediction error 8.8%. Entropy measures and RNN-based prediction also show competitive predictability, especially for unseen conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Entropy

Exploring methods for the automatic detection of errors in manual transcription

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arXiv 2019年

作者： Wang, Xiaofei Yang, Jinyi Li, Ruizhi Sadhu, Samik Hermansky, Hynek Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

Quality of data plays an important role in most deep learning tasks. In the speech community, transcription of speech recording is indispensable. Since the transcription is usually generated artificially, automatically finding errors in manual transcriptions not only saves time and labors but benefits the performance of tasks that need the training process. Inspired by the success of hybrid automatic speech recognition using both language model and acoustic model, two approaches of automatic error detection in the transcriptions have been explored in this work. Previous study using a biased language model approach, relying on a strong transcription-dependent language model, has been reviewed. In this work, we propose a novel acoustic model based approach, focusing on the phonetic sequence of speech. Both methods have been evaluated on a completely real dataset, which was originally transcribed with errors and strictly corrected manually afterwards. Copyright © 2019, The Authors. All rights reserved.

关键词： Error detection