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A multi-model method for short-utterance speaker recognition

A multi-model method for short-utterance speaker recognition

引用

Asia-Pacific Signal and Information processing Association Annual Summit and Conference 2011, APSIPA ASC 2011

作者： Zhang, Chenhao Wu, Xiaojun Wang, Linlin Wang, Gang Jang, Jyh-Shing Roger Zheng, Thomas Fang Department of Computer Science and Technology Center for Speech and Language Technologies Tsinghua University Beijing 100084 China Department of Computer Science Tsing Hua University Hsin-chu Taiwan

The length of the test speech greatly influences the performance of GMM-UBM based text-independent speaker recognition system, for example when the length of valid speech is as short as 1~5 seconds, the performance decreases significantly because the GMM-UBM based speaker recognition method is a statistical one, of which sufficient data is the foundation. Considering that the use of text information will be helpful to speaker recognition, a multi-model method is proposed to improve short-utterance speaker recognition (SUSR) in Chinese. We build a few phoneme class models for each speaker to represent different parts of the characteristic space and fuse the scores to fit the test data on the models with the purpose of increasing the matching degree between training models and test utterance. Experimental results showed that the proposed method achieved a relative EER reduction of about 26% compared with the traditional GMM-UBM method.

关键词： speech recognition

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A Graph-based Method for Entity Linking 5

A Graph-based Method for Entity Linking

引用

5th International Joint Conference on Natural language processing, IJCNLP 2011

作者： Guo, Yuhang Che, Wanxiang Liu, Ting Li, Sheng Research Center for Social Computing and Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9789744665645

In this paper, we formalize the task of finding a knowledge base entry that a given named entity mention refers to, namely entity linking, by identifying the most "important" node among the graph nodes representing the candidate entries. With the aim of ranking these entities by their "importance", we introduce three degree-based measures of graph connectivity. Experimental results on the TAC-KBP benchmark data sets show that our graph-based method performs comparably with the state-of-the-art methods. We also show that using the name phrase feature outperforms the commonly used bag-of-word feature for entity linking. © 2011 AFNLP

关键词： Knowledge based systems

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Improving Chinese POS Tagging with Dependency Parsing 5

Improving Chinese POS Tagging with Dependency Parsing

引用

5th International Joint Conference on Natural language processing, IJCNLP 2011

作者： Li, Zhenghua Che, Wanxiang Liu, Ting Research Center for Social Computing and Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9789744665645

Recent research usually models POS tagging as a sequential labeling problem, in which only local context features can be used. Due to the lack of morphological inflections, many tagging ambiguities in Chinese are difficult to handle unless consulting larger contexts. In this paper, we try to improve Chinese POS tagging by using long-distance dependencies produced by a statistical dependency parser. Experimental results show that, despite error propagation, the syntactic features can significantly improve the tagging accuracy from 93.88% to 94.41% (p -5). Detailed analysis shows that these features are helpful for ambiguous pairs like {NN,VV} and {DEC,DEG}. © 2011 AFNLP

关键词： Syntactics

来源：评论

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Word Sense Disambiguation Corpora Acquisition via Confirmation Code 5

Word Sense Disambiguation Corpora Acquisition via Confirmati...

引用

5th International Joint Conference on Natural language processing, IJCNLP 2011

作者： Che, Wanxiang Liu, Ting Research Center for Social Computing and Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9789744665645

Word Sense Disambiguation (WSD) is one of the fundamental natural language processing tasks. However, lack of training corpora is a bottleneck to construct a high accurate all-words WSD system. Annotating a large-scale corpus by experts costs enormous time and financial resources. Human Computation is a novel idea for integrating human resources behind the Web, which has been wasted, to solve practical problems that are difficult for computers. Based on human computation, we design a confirmation code system, which can not only distinguish between human beings and computers (the function of normal confirmation code system), but also annotate WSD corpora. The preliminary experimental result shows that the proposed method can annotate large-scale and high-quality WSD corpora within a short time. To the best of our knowledge, this is the first attempt to use confirmation code in natural language processing for corpora acquisition. © 2011 AFNLP

关键词： Natural language processing systems

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Generating Chinese Named Entity Data from a Parallel Corpus 5

Generating Chinese Named Entity Data from a Parallel Corpus

引用

5th International Joint Conference on Natural language processing, IJCNLP 2011

作者： Fu, Ruiji Qin, Bing Liu, Ting Research Center for Social Computing and Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9789744665645

Annotating Named Entity Recognition (NER) training corpora is a costly process but necessary for supervised NER systems. This paper presents an approach to generate large-scale Chinese NER training data from an English-Chinese discourse level aligned parallel corpus. Difficulty of NER is different among languages due to their unique features. For example, the performance of English NER systems is usually higher than the Chinese ones on average. In our method, we first employ a high performance NER system on one side of a bilingual corpus. And then, we project the NE labels to the other side according to the word level alignment. At last, we select high-quality labeled sentences using different strategies and generate an NER training corpus. In our experiments, we generate a Chinese NER corpus with 167,100 sentences through an English-Chinese parallel corpus. The system trained on the automatically generated corpus attains a comparable result with the one trained on the manually-annotated corpus. Further experiments show that the NER performance is significantly improved on two different evaluation sets by using the generated training data as an additional corpus to the manually-labeled data. © 2011 AFNLP

关键词： Natural language processing systems

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Randomized maximum entropy language models

Randomized maximum entropy language models

引用

2011 IEEE Workshop on Automatic speech Recognition and Understanding, ASRU 2011

作者： Xu, Puyang Khudanpur, Sanjeev Gunawardana, Asela Department of Electrical and Computer Engineering Center of Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Microsoft Research Redmond WA 98052 United States

ISBN: (纸本)9781467303675

We address the memory problem of maximum entropy language models(MELM) with very large feature sets. Randomized techniques are employed to remove all large, exact data structures in MELM implementations. To avoid the dictionary structure that maps each feature to its corresponding weight, the feature hashing trick [1] [2] can be used. We also replace the explicit storage of features with a Bloom filter. We show with extensive experiments that false positive errors of Bloom filters and random hash collisions do not degrade model performance. Both perplexity and WER improvements are demonstrated by building MELM that would otherwise be prohibitively large to estimate or store. © 2011 IEEE.

关键词： Data structures

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Efficient subsampling for training complex language models

Efficient subsampling for training complex language models

引用

Conference on Empirical Methods in Natural language processing, EMNLP 2011

作者： Xu, Puyang Gunawardana, Asela Khudanpur, Sanjeev Department of Electrical and Computer Engineering Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Microsoft Research Redmond WA 98052 United States

ISBN: (纸本)1937284115

We propose an efficient way to train maximum entropy language models (MELM) and neural network language models (NNLM). The advantage of the proposed method comes from a more robust and efficient subsampling technique. The original multi-class language modeling problem is transformed into a set of binary problems where each binary classifier predicts whether or not a particular word will occur. We show that the binarized model is as powerful as the standard model and allows us to aggressively subsample negative training examples without sacrificing predictive performance. Empirical results show that we can train MELM and NNLM at 1% ∼ 5% of the standard complexity with no loss in performance. © 2011 Association for Computational Linguistics.

关键词： Modeling languages

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Asymmetric acoustic model for accented speech recognition

Asymmetric acoustic model for accented speech recognition

引用

Asia-Pacific Signal and Information processing Association Annual Summit and Conference 2011, APSIPA ASC 2011

作者： Zhang, Chao Liu, Yi Zheng, Thomas Fang Tsinghua National Laboratory for Information Science and Technology Division of Technology Innovation and Development Center for Speech and Language Technologies Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

We propose to improve accented speech recognition performance by using asymmetric acoustic model. Our proposed model is generated based on reliable accent specific units and acoustic model reconstruction. The reliable units are extracted with time alignment recognition to cover accent variations at both acoustic and phonetic levels. The asymmetric acoustic model is obtained through selective decision tree merging together with dynamic Gaussian component selection in model reconstruction. The improved resolution of our proposed model is able to handle different levels of accented variations at different degrees. The effectiveness of our approach was evaluated on a typical Chinese accent. Our system outperforms traditional acoustic model reconstruction and MAP adaptation approaches by 8.28% and 7.14%, relatively on Syllable Error Rate (SER) reduction without sacrificing the performance on standard Mandarin speech.

关键词： speech recognition

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Radial Gaussianization with cluster-specific bias compensation

Radial Gaussianization with cluster-specific bias compensati...

引用

IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： Shuai Huang Damianos Karakos Daguang Xu Center of Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Human Language Technology Center of Excellence Center of Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

In recent work, Lyu and Simoncelli [1] introduced radial Gaussianization (RG) as a very efficient procedure for transforming n-dimensional random vectors into Gaussian vectors with independent and identically distributed (i.i.d.) components. This entails transforming the norms of the data so that they become chi-distributed with n degrees of freedom. A necessary requirement is that the original data are generated by an isotropic distribution, that is, their probability density function (pdf) is constant over surfaces of n-dimensional spheres (or, more general, n-dimensional ellipsoids). The case of biases in the data, which is of great practical interest, is studied here; as we demonstrate with experiments, there are situations in which even very small amounts of bias can cause RG to fail. This becomes evident especially when the data form clusters in low-dimensional manifolds. To address this shortcoming, we propose a two-step approach which entails (i) first discovering clusters in the data and removing the bias from each, and (ii) performing RG on the bias-compensated data. In experiments with synthetic data, the proposed bias compensation procedure results in significantly better Gaus-sianization than the non-compensated RG method.

关键词： Principal component analysis Noise measurement Estimation Gaussian distribution Distributed databases Optimization Transforms

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An in-car Chinese noise corpus for speech recognition

An in-car Chinese noise corpus for speech recognition

引用

2011 International Conference on Asian language processing, IALP 2011

作者： Hou, Jue Liu, Yi Zhang, Chao Huang, Shilei Center for Speech and Language Technologies Division of Technology Innovation and Development Tsinghua National Laboratory for Information Science and Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Shenzhen Key Laboratory of Intelligent Media and Speech Shenzhen China

ISBN: (纸本)9780769545547

In this paper, we present an in-car Chinese noise corpus that can be used in simulating complicated car environment for robust speech recognition research and experiment. The corpus was collected in mainland China in 2009 and 2010. The corpus includes a diversity of car conditions including different car speed, open/close windows, weather conditions as well as environment conditions. Specially, the rumble strips are also taken into account due to the typical noise generated as the car is passing on. In order to use the corpus efficiently, we performed some acoustic signal analyses on those noise data, mainly focused on stationary properties and energy distribution in the frequency domain. We also performed ASR experiments using selected noise data from the corpus, by adding noise data to clean speech to simulate the in-car environment. The corpus is the first of its kind for in-car Chinese noise corpus, providing abundant and diversified samples for car noise speech recognition task. © 2011 IEEE.

关键词： speech recognition

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