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Grouping of Handwritten Bangla Basic Characters, Numerals and Vowel Modifiers for Multilayer Classification

Grouping of Handwritten Bangla Basic Characters, Numerals an...

引用

International Workshop on Frontiers in Handwriting Recognition

作者： Khondker Nayef Reza Mumit Khan Center for Research on Bangla Language Processing Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

For better performance in multilayer or hierarchical classification of handwritten text, appropriate grouping of similar symbols is very important. Here we aim to develop a reliable grouping schema for the similar looking basic characters, numerals and vowel modifiers of Bangla language. We experimented with thickened and thinned segmented handwritten text to compare which type of image is better for which group. For classification we chose Support Vector Machine (SVM) as it outperforms other classifiers in this field. We used both “one against one” and “one against all” strategies for multiclass SVM and compared their performance.

关键词： Support vector machines Handwriting recognition Character recognition Accuracy Training Compounds

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Short Utterance Speaker Recognition A research Agenda

Short Utterance Speaker Recognition A research Agenda

引用

International Conference on Systems and Informatics (ICSAI)

作者： Nakhat Fatima Thomas Fang Zheng Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

Short Utterance Speaker Recognition (SUSR) is an important area of speaker recognition when only small amount of speech data is available for testing and training. We list the most commonly used state-of-the-art methods of speaker recognition and the significance of prosodic speaker recognition. A short survey of SUSR is hereby conducted, highlighting various methodologies when using short utterances to recognize speakers. We also specify future research directions in the field SUSR which, together with modern technologies and the ongoing research in prosodic speaker recognition, can lead to better results in speaker recognition.

关键词： Speaker recognition speech Hidden Markov models speech recognition Acoustics Training Humans

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Vowel-category based Short Utterance Speaker Recognition

Vowel-category based Short Utterance Speaker Recognition

引用

International Conference on Systems and Informatics (ICSAI)

The impact of Short Utterances in Speaker Recognition is of significant importance. Despite the advancements in short utterance speaker recognition (SUSR), text dependence and the role of phonemes in carrying speaker information needs further investigation. This paper presents a novel method of using vowel categories for SUSR. We define Vowel Categories (VC's) considering Chinese and English languages. After recognition and extraction of phonemes, the obtained vowels are divided into VC's, which are then used to develop Universal Background VC Models (UBVCM) for each VC. Conventional GMM-UBM system is used for training and testing. The proposed categories give minimum EERs of 13.76%, 14.03% and 16.18% for 3, 2 and 1 second respectively. Experimental results show that in text dependent SUSR, significant speaker-specific information is present at phoneme level. The similar properties of phonemes can be used such that accurate speech recognition is not required, rather Phoneme Categories can be used effectively for SUSR. Also, it is shown that vowels contain large amount of speaker information, which remains undisturbed when VC are employed.

关键词： Speaker recognition speech speech recognition Training Testing Feature extraction Hidden Markov models

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Syllable category based short utterance speaker recognition

Syllable category based short utterance speaker recognition

引用

International Conference on Audio, language and Image processing, ICALIP

In Short Utterance Speaker Recognition (SUSR), the role of complete speech units like syllables in carrying speaker information needs further investigation. This paper presents a novel method of using syllable categories for SUSR. We define Syllable Categories (SCs) with the help of syllable structure of Chinese language. Syllables in speech are segmented into SCs, which are then used to develop Universal Background SC Model for each SC. Conventional GMM-UBM system is used for training and testing. The proposed categories give average EER of 17.79%, 19.35% and 21.65% for 3, 2 and 1 second of test utterance length respectively. Experimental results show that in text dependent SUSR, significant speaker-specific information is present at syllable level where prosodic idiosyncrasies can be utilized. This information can be used in SUSR by exploiting similarities in consonants and vowels of a syllable such that SCs can be used effectively.

关键词： Speaker recognition speech speech recognition Training Feature extraction Hidden Markov models Liquids

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Descriptive question answering with answer type independent features

Descriptive question answering with answer type independent ...

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作者： Yoon, Yeo-Chan Lee, Chang-Ki Kim, Hyun-Ki Jang, Myung-Gil Ryu, Pum Mo Park, So-Young Speech/Language Information Research Center ETRI Gajeong-dong Yuseong-gu Daejon Korea Republic of Department of Computer Science Kangwon National University 192-1 Hyoja2-dong Chuncheon-si Gangwon-do Korea Republic of Division of Digital Media Technology SangMyung University 7 Hongji-dong Jongro-gu Seoul Korea Republic of

In this paper, we present a supervised learning method to seek out answers to the most frequently asked descriptive questions: reason, method, and definition questions. Most of the previous systems for question answering focus on factoids, lists or definitional questions. However, descriptive questions such as reason questions and method questions are also frequently asked by users. We propose a system for these types of questions. The system conducts an answer search as follows. First, we analyze the user's question and extract search keywords and the expected answer type. Second, information retrieval results are obtained from an existing search engine such as Yahoo or Google. Finally, we rank the results to find snippets containing answers to the questions based on a ranking SVM algorithm. We also propose features to identify snippets containing answers for descriptive questions. The features are adaptable and thus are not dependent on answer type. Experimental results show that the proposed method and features are clearly effective for the task. © 2012 The Institute of Electronics, Information and Communication Engineers.

关键词： Search engines

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Text-Dependent Speaker Recognition with long-term features based on functional data analysis

Text-Dependent Speaker Recognition with long-term features b...

引用

International Symposium on Chinese Spoken language processing

作者： Chenhao Zhang Thomas Fang Zheng Ruxin Chen Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Research and Development Sony Computer Entertainment America Foster City CA USA

Text-Dependent Speaker Recognition (TDSR) is widely used nowadays. The short-term features like Mel-Frequency Cepstral Coefficient (MFCC) have been the dominant features used in traditional Dynamic Time Warping (DTW) based TDSR systems. The short-term features capture better local portion of the significant temporal dynamics but worse in overall sentence statistical characteristics. Functional Data Analysis (FDA) has been proven to show significant advantage in exploring the statistic information of data, so in this paper, a long-term feature extraction based on MFCC and FDA theory is proposed, where the extraction procedure consists of the following steps: Firstly, the FDA theory is applied after the MFCC feature extraction; Secondly, for the purpose of compressing the redundant data information, new feature based on the Functional Principle Component Analysis (FPCA) is generated; Thirdly, the distance between train features and test features is calculated for the use of the recognition procedure. Compared with the existing MFCC plus DTW method, experimental results show that the new features extracted with the proposed method plus the cosine similarity measure demonstrates better performance.

关键词： Mel frequency cepstral coefficient Feature extraction speech speech recognition Speaker recognition Data analysis Fitting

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MAISE: A Flexible, Configurable, Extensible Open Source Package for Mass AI System Evaluation 6

MAISE: A Flexible, Configurable, Extensible Open Source Pack...

引用

6thWorkshop on Statistical Machine Translation, WMT 2011

作者： Zaidan, Omar F. Dept. of Computer Science The Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284121

The past few years have seen an increasing interest in using Amazon's Mechanical Turk for purposes of collecting data and performing annotation tasks. One such task is the mass evaluation of system output in a variety of tasks. In this paper, we present MAISE, a package that allows researchers to evaluate the output of their AI system(s) using human judgments collected via Amazon's Mechanical Turk, greatly streamlining the process. MAISE is open source, easy to run, and platform-independent. The core of MAISE's codebase was used for the manual evaluation of WMT10, and the completed package is being used again in the current evaluation for WMT11. In this paper, we describe the main features, functionality, and usage of MAISE, which is now available for download and use. © 2011 Association for Computational Linguistics

关键词： Open systems

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Multistream robust speaker recognition based on speech intelligibility

Multistream robust speaker recognition based on speech intel...

引用

Annual Conference on Information sciences and Systems

作者： Nemala, Sridhar Krishna Elhilali, Mounya Department of Electrical and Computer Engineering Center for Speech and Language Processing Johns Hopkins University Baltimore MD 21218 United States

ISBN: (纸本)9781424498475

Delimiting the most informative voice segments of an acoustic signal is often a crucial initial step for any speech processing system. In the current work, we propose a novel segmentation approach based on a perception-based measure of speech intelligibility. Unlike segmentation approaches based on various forms of voice-activity detection (VAD), the proposed segmentation approach exploits higher-level perceptual information about the signal intelligibility levels. This classification based on intelligibility estimates is integrated into a novel multistream framework for automatic speaker recognition task. The multistream system processes the input acoustic signal along multiple independent streams reflecting various levels of intelligibility and then fusing the decision scores from the multiple steams according to their intelligibility contribution. Our results show that the proposed multistream system achieves significant improvements both in clean and noisy conditions when compared with a baseline and a state-of-the-art voice-activity detection algorithm. © 2011 IEEE.

关键词： speech intelligibility

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Discrimination-emphasized mel-frequency-warping for time-varying speaker recognition

Discrimination-emphasized mel-frequency-warping for time-var...

引用

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

作者： Wang, Linlin Zheng, Thomas Fang Zhang, Chenhao Wang, Gang Department of Computer Science and Technology Center for Speech and Language Technologies Tsinghua University Beijing 100084 China

Performance degradation with time varying is a generally acknowledged phenomenon in speaker recognition and it is widely assumed that speaker models should be updated from time to time to maintain representativeness. However, it is costly, user-unfriendly, and sometimes, perhaps unrealistic, which hinders the technology from practical applications. From a pattern recognition point of view, the time-varying issue in speaker recognition requires such features that are speakerspecific, and as stable as possible across time-varying sessions. Therefore, after searching and analyzing the most stable parts of feature space, a Discrimination-emphasized Mel-frequencywarping method is proposed. In implementation, each frequency band is assigned with a discrimination score, which takes into account both speaker and session information, and Melfrequency- warping is done in feature extraction to emphasize bands with higher scores. Experimental results show that in the time-varying voiceprint database, this method can not only improve speaker recognition performance with an EER reduction of 19.1%, but also alleviate performance degradation brought by time varying with a reduction of 8.9%.

关键词： speech recognition

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SDBM-based speaker recognition for speaking style variations

SDBM-based speaker recognition for speaking style variations

引用

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

作者： Wang, Linlin Xu, Mingxing Department of Computer Science and Technology Center for Speech and Language Technologies Tsinghua University Beijing 100084 China

There are many factors corresponding to performance degradation of an actual speaker recognition system. Mismatch in speaking style of a target speaker during training and testing is an important one. When a client enrolls in a system, it is natural for him/her to speak in a spontaneous way. However, it is difficult to maintain the same speaking style throughout test phases. In view of this situation, this paper, based on a database with multiple speaking styles, proposes the concept of Speaking-style-Dependent Background Model (SDBM). The SDBM-based system is presented to train speaking style featured speaker models aiming to alleviate the speaking style mismatch between training and testing. Experimental results show that EER can be reduced by 35.40%.

关键词： speech recognition

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