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Sign language Gesture Classification Using Neural Networks 4

Sign Language Gesture Classification Using Neural Networks

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4th International Conference on Advances in Speech and language Technologies for Iberian languages, IberSPEECH 2018

作者： Parcheta, Zuzanna Martínez-Hinarejos, Carlos-D. Sciling S.L. Carrer del Riu 321 Pinedo 46012 Spain Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n 46022 Spain

Recent studies have demonstrated the power of neural networks for different fields of artificial intelligence. In most fields, such as machine translation or speech recognition, neural networks outperform previously used methods (Hidden Markov Models with Gaussian Mixtures, Statistical Machine Translation, etc.). In this paper, the efficiency of the LeNet convolutional neural network for isolated word sign language recognition is demonstrated. As a preprocessing step, we apply several techniques to obtain the same dimension for the input that contains gesture information. The performance of these preprocessing techniques on a Spanish Sign language dataset is evaluated. These approaches outperform previously obtained results based on Hidden Markov Models. © 4th International Conference, IberSPEECH 2018.

关键词： Gesture recognition

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Articulatory motivated acoustic features for speech recognition

Articulatory motivated acoustic features for speech recognit...

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9th European Conference on Speech Communication and technology

作者： Kocharov, Daniil Zolnay, András Schlüter, Ralf Ney, Hermann Department of Phonetics Faculty of Philology Saint-Petersburg State University 199034 Saint Petersburg Russia Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University 52056 Aachen Germany

In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative feature. The new feature is tested in combination with the standard Mel Frequency Cepstral Coefficients (MFCC) and the voicedness features. Linear Discriminant Analysis is applied to find the optimal combination of different acoustic features. Experiments have been performed on small and large vocabulary tasks. Significant improvements in word error rate have been obtained by combining the MFCC feature with the articulatory motivated voicedness and spectrum derivative features: improvements of up to 25% on the small-vocabulary task and improvements of up to 4% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.

关键词： Speech recognition

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Performance analysis of Neural Networks in combination with n-gram language models

Performance analysis of Neural Networks in combination with ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ilya Oparin Martin Sundermeyer Hermann Ney Jean-Luc Gauvain LIMSI CNRS Spoken Language Processing Group France Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Germany

Neural Network language models (NNLMs) have recently become an important complement to conventional n-gram language models (LMs) in speech-to-text systems. However, little is known about the behavior of NNLMs. The analysis presented in this paper aims to understand which types of events are better modeled by NNLMs as compared to n-gram LMs, in what cases improvements are most substantial and why this is the case. Such an analysis is important to take further benefit from NNLMs used in combination with conventional n-gram models. The analysis is carried out for different types of neural network (feed-forward and recurrent) LMs. The results showing for which type of events NNLMs provide better probability estimates are validated on two setups that are different in their size and the degree of data homogeneity.

关键词： Artificial neural networks History Analytical models Training data Vocabulary Interpolation

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How Much Does Tokenization Affect Neural Machine Translation? 1

引用

20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Domingo, Miguel García-Martínez, Mercedes Helle, Alexandre Casacuberta, Francisco Herranz, Manuel Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain Pangeanic/B.I Europa PangeaMT Technologies Division Valencia Spain

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

Tokenization or segmentation is a wide concept that covers simple processes such as separating punctuation from words, or more sophisticated processes such as applying morphological knowledge. Neural Machine Translation (NMT) requires a limited-size vocabulary for computational cost and enough examples to estimate word embeddings. Separating punctuation and splitting tokens into words or subwords has proven to be helpful to reduce vocabulary and increase the number of examples of each word, improving the translation quality. Tokenization is more challenging when dealing with languages with no separator between words. In order to assess the impact of the tokenization in the quality of the final translation on NMT, we experimented on five tokenizers over ten language pairs. We reached the conclusion that the tokenization significantly affects the final translation quality and that the best tokenizer differs for different language pairs. © 2023, Springer Nature Switzerland AG.

关键词： Neural machine translation

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Prediction of LSTM-RNN Full Context States as a Subtask for N-Gram Feedforward language Models

Prediction of LSTM-RNN Full Context States as a Subtask for ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kazuki Irie Zhihong Lei Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University D-52056 Aachen Germany

ISBN: (纸本)9781538646595

Long short-term memory (LSTM) recurrent neural network language models compress the full context of variable lengths into a fixed size vector. In this work, we investigate the task of predicting the LSTM hidden representation of the full context from a truncated n-gram context as a subtask for training an n-gram feedforward language model. Since this approach is a form of knowledge distillation, we compare two methods. First, we investigate the standard transfer based on the Kullback-Leibler divergence of the output distribution of the feedforward model from that of the LSTM. Second, we minimize the mean squared error between the hidden state of the LSTM and that of the n-gram feedforward model. We carry out experiments on different subsets of the Switchboard speech recognition dataset for feedforward models with a short (5-gram) and a medium (10-gram) context length. We show that we get improvements in perplexity and word error rate of up to 8% and 4% relative for the medium model, while the improvements are only marginal for the short model.

关键词： language modeling neural networks knowledge distillation student-teacher speech recognition modelling languages Speech recognition FEEDFORWARD Neural network Error analysis

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INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER MATCHED AND MISMATCHED ACOUSTICAL CONDITIONS

INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER ...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Joel Pinto Daniel Willett Ralf Schluter Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Nuance Communications Deutschland GmbH

ISBN: (纸本)9781479903573

In this paper, Multi Layer Perceptron (MLP) based multilingual bottleneck features are investigated for acoustic modeling in three languages -- German, French, and US English. We use a modified training algorithm to handle the multilingual training scenario without having to explicitly map the phonemes to a common phoneme set. Furthermore, the cross-lingual portability of bottleneck features between the three languages are also investigated. Single pass recognition experiments on large vocabulary SMS dictation task indicate that (1) multilingual bottleneck features yield significantly lower word error rates compared to standard MFCC features (2) multilingual bottleneck features are superior to monolingual bottleneck features trained for the target language with limited training data, and (3) multilingual bottleneck features are beneficial in training acoustic models in a low resource language where only mismatched training data is available - by exploiting the more matched training data from other languages.

关键词： MLP Bottleneck Multilingual Mismatched acoustical condition Traffic Bottlenecks CSRP3 gene Multilingualism Object oriented programming languages language acoustic modeling Training data Phonemes Error analysis

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Training language Models for Long-Span Cross-Sentence Evaluation

Training Language Models for Long-Span Cross-Sentence Evalua...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Kazuki Irie Albert Zeyer Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

While recurrent neural networks can motivate cross-sentence language modeling and its application to automatic speech recognition (ASR), corresponding modifications of the training method for that end are rarely discussed. In fact, even more generally, the impact of training sequence construction strategy in language modeling for different evaluation conditions is typically ignored. In this work, we revisit this basic but fundamental question. We train language models based on long short-term memory recurrent neural networks and Transformers using various types of training sequences and study their robustness with respect to different evaluation modes. Our experiments on 300h Switchboard and Quaero English datasets show that models trained with back-propagation over sequences consisting of concatenation of multiple sentences with state carry-over across sequences effectively outperform those trained with the sentence-level training, both in terms of perplexity and word error rates for cross-utterance ASR.

关键词： Training Context modeling Computational modeling Recurrent neural networks Encoding Switches Standards

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Moment-Based Image Normalization for Handwritten Text Recognition

Moment-Based Image Normalization for Handwritten Text Recogn...

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International Workshop on Frontiers in Handwriting Recognition

作者： Michal Kozielski Jens Forster Hermann Ney Human Language Technology and Pattern Recognition Group Chair of Computer Science 6 RWTH Aachen University Aachen Germany

In this paper, we extend the concept of moment-based normalization of images from digit recognition to the recognition of handwritten text. Image moments provide robust estimates for text characteristics such as size and position of words within an image. For handwriting recognition the normalization procedure is applied to image slices independently. Additionally, a novel moment-based algorithm for line-thickness normalization is presented. The proposed normalization methods are evaluated on the RIMES database of French handwriting and the IAM database of English handwriting. For RIMES we achieve an improvement from 16.7% word error rate to 13.4% and for IAM from 46.6% to 37.3%.

关键词： Hidden Markov models Databases Shape Image segmentation Vectors Handwriting recognition Error analysis

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Tight Integrated End-to-End Training for Cascaded Speech Translation

Tight Integrated End-to-End Training for Cascaded Speech Tra...

引用

IEEE Spoken language technology Workshop

作者： Parnia Bahar Tobias Bieschke Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9781728170664

ISBN: (纸本)9781728170671

A cascaded speech translation model relies on discrete and non-differentiable transcription, which provides a supervision signal from the source side and helps the transformation between source speech and target text. Such modeling suffers from error propagation between ASR and MT models. Direct speech translation is an alternative method to avoid error propagation; however, its performance is often behind the cascade system. To use an intermediate representation and preserve the end-to-end trainability, previous studies have proposed using two-stage models by passing the hidden vectors of the recognizer into the decoder of the MT model and ignoring the MT encoder. This work explores the feasibility of collapsing the entire cascade components into a single end-to-end trainable model by optimizing all parameters of ASR and MT models jointly without ignoring any learned parameters. It is a tightly integrated method that passes renormalized source word posterior distributions as a soft decision instead of one-hot vectors and enables backpropagation. Therefore, it provides both transcriptions and translations and achieves strong consistency between them. Our experiments on four tasks with different data scenarios show that the model outperforms cascade models up to 1.8% in BLEU and 2.0% in TER and is superior compared to direct models.

关键词： Training Adaptation models Correlation Training data Data models Decoding Task analysis

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Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling Using a Two-Dimensional Grid

Two-Way Neural Machine Translation: A Proof of Concept for B...

引用

IEEE Spoken language technology Workshop

作者： Parnia Bahar Christopher Brix Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

ISBN: (数字)9781728170664

ISBN: (纸本)9781728170671

Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not easy to get the best effect for bidirectional translation, i.e., both source-to-target and target-to-source translation using a single model. If we exclude some pioneering attempts, such as multilingual systems, all other bidirectional translation approaches are required to train two individual models. This paper proposes to build a single end-to-end bidirectional translation model using a two-dimensional grid, where the left-to-right decoding generates source-to-target, and the bottom-to-up decoding creates target-to-source output. Instead of training two models independently, our approach encourages a single network to jointly learn to translate in both directions. Experiments on the WMT2018 German↔English and Turkish↔English translation tasks show that the proposed model is capable of generating a good translation quality and has sufficient potential to direct the research.

关键词： Training Conferences Decoding Machine translation Task analysis

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