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Using Class Purity as Criterion for Speaker Clustering in Multi-Speaker Detection Tasks

Using Class Purity as Criterion for Speaker Clustering in Mu...

引用

2011年亚太信号与信息处理协会年会

作者： 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

Speaker clustering is an important step in multispeaker detection tasks and its performance directly affects the speaker detection performance. It is observed that the shorter the average length of single-speaker speech segments after segmentation is, the worse performance of the following speaker recognition will be achieved, therefore a reasonable solution to better multi-speaker detection performance is to enlarge the average length of after-segmentation single-speaker speech segments, which is equivalently to cluster as many true samespeaker segments into one as possible. In other words, the average class purity of each speaker segment should be as bigger as possible. Accordingly, a speaker-clustering algorithm based on the class purity criterion is proposed, where a Reference Speaker Model (RSM) scheme is adopted to calculate the distance between speech segments, and the maximal class purity, or equivalently the minimal within-class dispersion, is taken as the criterion. Experiments on the NIST SRE 2006 database showed that, compared with the conventional Hierarchical Agglomerative Clustering (HAC) algorithm, for speech segments with average lengths of 2 seconds, 5 seconds and 8 seconds, the proposed algorithm increased the valid class speech length by 2.7%, 3.8% and 4.6%, respectively, and finally the target speaker detection recall rate was increased by 7.6%, 6.2% and 5.1%, respectively.

关键词： SRE Using Class Purity as Criterion for Speaker Clustering in Multi-Speaker Detection Tasks

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Detection-based accented speech recognition using articulatory features

Detection-based accented speech recognition using articulato...

引用

IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Chao Zhang Yi Liu Chin-Hui Lee Center for Speech and Language Technologies Division of Technology Innovation and Development Tsinghua National Laboratory for Science and Technology Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

We propose an attribute-based approach to accented speech recognition based on automatic speech attribute transcription with high efficiency detection of articulatory features. In order to utilize appropriate and extensible phonetic and linguistic knowledge, conditional random field (CRF) is designed to take frame-level inputs with binary feature functions. The use of CRF with merely the state features to generate probabilistic phone lattices is then utilized to solve the phone under-generation problem. Finally an attribute discrimination module is incorporated to handle a diversity of accent changes without retraining any model, leading to flexible “plug `n' play” modular design. The effectiveness of the proposed approach is evaluated on three typical Chinese accents, namely Guanhua, Yue and Wu. Our method yields a significant absolute phone recognition accuracy improvement 5.04%, 4.68% and 6.06% for the corresponding three accent types over a conventional monophone HMM system. Compared to a context-dependent triphone HMM system, we achieve comparable phone accuracies at only less than 20% of the computation cost. In addition, our proposed method is equally applicable to speaker-independent systems handling multiple accents.

关键词： Hidden Markov models Detectors Feature extraction Speech recognition Acoustics Speech Corporate acquisitions

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An In-car Chinese Noise Corpus for Speech Recognition

An In-car Chinese Noise Corpus for Speech Recognition

引用

International Conference on Asian language Processing (IALP)

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

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.

关键词： Noise Speech Speech recognition Accuracy Databases Roads Noise measurement

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Reliable accent specific unit generation with dynamic Gaussian mixture selection for multi-accent speech recognition

Reliable accent specific unit generation with dynamic Gaussi...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Chao Zhang Yi Liu Yunqing Xia Thomas Fang Zheng Jesper Olsen JiLei Tian Department of Computer Science and Technology Tsinghua University Beijing China Center of Speech and Language Technologies Division of Technology Innovation and Development Tsinghua National Laboratory for Information Science and Technology Beijing China Nokia Research Center Beijing China

Multiple accents are often present in Mandarin speech, as most Chinese have learned Mandarin as a second language. We propose generating reliable accent specific unit together with dynamic Gaussian mixture selection for multi-accent speech recognition. Time alignment phoneme recognition is used to generate such unit and to model accent variations explicitly and accurately. Dynamic Gaussian mixture selection scheme builds a dynamical observation density for each specified frame in decoding, and leads to use Gaussian mixture component efficiently. This method increases the covering ability for a diversity of accent variations in multi-accent, and alleviates the performance degradation caused by pruned beam search without augmenting the model size. The effectiveness of this approach is evaluated on three typical Chinese accents Chuan, Yue and Wu. Our approach outperforms traditional acoustic model reconstruction approach significantly by 6.30%, 4.93% and 5.53%, respectively on Syllable Error Rate (SER) reduction, without degrading on standard speech.

关键词： Acoustics Reliability Speech Hidden Markov models Speech recognition Decoding Adaptation models

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A Multi-Model Method for Short-Utterance Speaker Recognition

A Multi-Model Method for Short-Utterance Speaker Recognition

引用

2011年亚太信号与信息处理协会年会

作者： Jyh-Shing Roger Jang Thomas Fang Zheng Department of Computer Science Tsing Hua University Hsin-chu 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

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： GMM UBM A Multi-Model Method for Short-Utterance Speaker Recognition Model

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Using cepstral and prosodic features for Chinese accent identification

Using cepstral and prosodic features for Chinese accent iden...

引用

2010 7th International Symposium on Chinese Spoken language Processing, ISCSLP 2010

作者： Hou, Jue Liu, Yi Zheng, Thomas Fang Olsen, Jesper Tian, Jilei Center for Speech and Language Technologies Division of Technology Innovation and Development Tsinghua National Laboratory for Information Science and Technology Beijing China Nokia Research Center Beijing China

ISBN: (纸本)9781424462469

In this paper, we propose an approach for Chinese accent identification using both cepstral and prosodic features with gender-dependent model. We exploit a combination of conventional Shifted Delta Cepstrum (SDC) features and pitch contour features as an example of segmental and suprasegmental features, to capture the characteristics in Chinese accents. We use cubic polynomials to estimate the pitch contour segments in order to model the differences within accents. We train gender-dependent GMM acoustic models to express the features in order to deal with the gender variation. Since conventional criterion of the GMM assumption cannot solve those multi-feature problems, we use the support vector machine (SVM) to make the decision. We evaluated the effectiveness of the proposed approach on the 863 Chinese accent database. The result shows that our approach yields a 15.5% relative error rate reduction compared to conventional approaches of using only SDC features. ©2010 IEEE.

关键词： Support vector machines

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Multi-layered features with SVM for Chinese accent identification

Multi-layered features with SVM for Chinese accent identific...

引用

2010 International Conference on Audio, language and Image Processing, ICALIP 2010

ISBN: (纸本)9781424458653

In this paper, we propose an approach of multilayered feature combination associated with support vector machine (SVM) for Chinese accent identification. The multi-layered features include both segmental and suprasegmental information, such as MFCC and pitch contour, to capture the diversity of variations in Chinese accented speech. The pitch contour is estimated using cubic polynomial method to model the variant characters in different accents in Chinese. We train two GMM acoustic models in order to express the features of a certain accent. As the original criterion of the GMM model cannot deal with such multi-layered features, the SVM is utilized to make the decision. The effectiveness of the proposed approach was evaluated on the 863 Chinese accent corpus. Our approach yields a significant 10% relative error rate reduction compared with traditional approaches using sole feature at single level in Chinese accented speech identification. ©2010 IEEE.

关键词： Support vector machines

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BEST 2009 : Thai word segmentation software contest

BEST 2009 : Thai word segmentation software contest

引用

International Symposium on Natural language Processing (SNLP)

作者： Krit Kosawat Monthika Boriboon Patcharika Chootrakool Ananlada Chotimongkol Supon Klaithin Sarawoot Kongyoung Kanyanut Kriengket Sitthaa Phaholphinyo Sumonmas Purodakananda Tipraporn Thanakulwarapas Chai Wutiwiwatchai Human Language Technology Laboratory (HLT) National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Ministry of Science and Technology Klong Luang Pathum Thani Thailand

This is a non-technical paper describing how and why we organized BEST 2009, the first contest in the series of ldquobenchmark for enhancing the standard of Thai language processingrdquo, which is expected to help accelerate the progress of the natural language processing technology in Thailand by assembling 3 essential components: common standards, resources and researchers. The BEST 2009 : Thai word segmentation software contest is the first shared task on Thai NLP that exercised this assemblage and aimed to find the best algorithms that could correctly divide Thai non-segmented script into words according to the guidelines previously prepared by experts from several research institutes and universities. Thai word-segmented corpora of 5 million words have been developed as a training set, another 600 K as a test set. The evaluation procedure and protocol have been designed. The process and the results of the contest are reported.

关键词： Natural language processing Software standards Assembly Educational institutions Testing Acceleration Paper technology Software algorithms Guidelines Protocols

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Effectiveness of n-gram fast match for query-by-humming systems

Effectiveness of n-gram fast match for query-by-humming syst...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jue Hou Dan-ning Jiang Wen-xiao Cao Yong Qin Thomas Fang Zheng Yi Liu Center of Speech and Language Technologies Division of Technology Innovation and Development Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China IBM China Research Laboratory Beijing China

To achieve a good balance between matching accuracy and computation efficiency is a key challenge for query-by-humming (QBH) system. In this paper, we propose an approach of n-gram based fast match. Our n-gram method uses a robust statistical note transcription as well as error compensation method based on the analysis of frequent transcription errors. The effectiveness of our approach has been evaluated on a relatively large melody database with 5223 melodies. The experimental results show that when the searching space was reduced to only 10% of the whole size, 90% of the target melodies were preserved in the candidates, and 88% of the match accuracy of system was kept. Meanwhile, no obvious additional computation was applied.

关键词： Databases Computational efficiency Degradation Robustness Speech Natural languages Technological innovation Laboratories Information science Computer science

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A phrase-level piecewise linear scaling algorithm for melody match in Query-by-Humming systems

A phrase-level piecewise linear scaling algorithm for melody...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Wenxiao Cao Danning Jiang Jue Hou Yong Qin Thomas Fang Zheng Yi Liu Center of Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China IBM China Research Laboratory Beijing China

The Query-by-Humming (QBH) system allows users to retrieve songs by singing/humming. In this paper we propose a phrase-level piecewise linear scaling algorithm for melody match. Musical phrase boundaries are predicted for the query to split it to phrases. The boundaries of melody fragment corresponding to each phrase are allowed for adjusting in a limited scope. The algorithm employs Dynamic Programming and Recursive Alignment to search for the minimal piecewise matching cost upon Linear Scaling at phrase-level. Our experimental results on 5223 melody database show that the proposed algorithm outperforms traditional algorithms. The proposed algorithm gives significant improvements of 17.0%, 14.7% and 4.8% with respect to Linear Scaling, Dynamic Time Wrapping and Recursive Alignment in top-1 rate, respectively. The results show that the proposed algorithm is more efficient than the previous algorithms.

关键词： Piecewise linear techniques Rhythm Natural languages Dynamic programming Costs Heuristic algorithms Databases Speech Technological innovation Laboratories

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