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Unsupervised learning of acoustic sub-word units

Unsupervised learning of acoustic sub-word units

引用

46th Annual Meeting of the Association for Computational Linguistics: Human language Technologies, ACL-08: HLT

作者： Varadarajan, Balakrishnan Khudanpur, Sanjeev Dupoux, Emmanuel Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Laboratoire de Science Cognitive et Psycholinguistique 75005 Paris France

ISBN: (纸本)9781932432046

Accurate unsupervised learning of phonemes of a language directly from speech is demonstrated via an algorithm for joint unsupervised learning of the topology and parameters of a hidden Markov model (HMM);states and short state-sequences through this HMM correspond to the learnt sub-word units. The algorithm, originally proposed for unsupervised learning of allophonic variations within a given phoneme set, has been adapted to learn without any knowledge of the phonemes. An evaluation methodology is also proposed, whereby the state-sequence that aligns to a test utterance is transduced in an automatic manner to a phoneme-sequence and compared to its manual transcription. Over 85% phoneme recognition accuracy is demonstrated for speaker-dependent learning from fluent, large-vocabulary speech. © 2008 Association for Computational Linguistics.

关键词： Unsupervised learning

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Unsupervised learning of acoustic sub-word units 46

Unsupervised learning of acoustic sub-word units

引用

46th Annual Meeting of the Association for Computational Linguistics: Human language Technologies, ACL 2008

作者： Varadarajan, Balakrishnan Khudanpur, Sanjeev Dupoux, Emmanuel Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Laboratoire de Science Cognitive et Psycholinguistique Paris75005 France

关键词： Unsupervised learning

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Computing word-pair antonymy

Computing word-pair antonymy

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2008 Conference on Empirical Methods in Natural language processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken language Translation

作者： Mohammad, Saif Dorr, Bonnie Hirst, Graeme Laboratory for Computational Linguistics and Information Processing Institute for Advanced Computer Studies University of Maryland United States Computer Science Human Language Technology Center of Excellence United States Department of Computer Science University of Toronto Canada

Knowing the degree of antonymy between words has widespread applications in natural language processing. Manually-created lexicons have limited coverage and do not include most semantically contrasting word pairs. We present a new automatic and empirical measure of antonymy that combines corpus statistics with the structure of a published thesaurus. The approach is evaluated on a set of closest-opposite questions, obtaining a precision of over 80%. Along the way, we discuss what humans consider antonymous and how antonymy manifests itself in utterances. © 2008 Association for Computational Linguistics.

关键词： Natural language processing systems

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A Two-Stage Multi-Feature Integration Approach to Unsupervised Speaker Change Detection in Real-Time News Broadcasting

A Two-Stage Multi-Feature Integration Approach to Unsupervis...

引用

International Symposium on Chinese Spoken language processing

作者： Lei Xie Guangsen Wang Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781424429424

This paper presents a two-stage multi-feature integration approach for unsupervised speaker change detection in real-time news broadcasting. We integrate MFCC and LSP features (i.e. a perceptual feature plus a articulatory feature) in the metric-based potential speaker change detection stage to collect speaker boundary candidates as many as possible. We adopt a weighted Bayesian information criterion (BIC) to integrate boundary decisions from MFCC and LSP features in the speaker boundary confirmation stage. This multi-feature integration strategy makes use of the complementarity between perceptual features and articulatory features to achieve a performance gain. Speaker change detection experiments show that the multi- feature integration approach significantly outperforms the individual features with relative improvements of 26% over the LSP-only approach and 6% over the MFCC-only approach.

关键词： Broadcasting Loudspeakers Hidden Markov models Mel frequency cepstral coefficient Streaming media speech processing Bayesian methods speech recognition Acoustic signal detection Decoding

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Subword Latent Semantic Analysis for Texttiling-Based Automatic Story Segmentation of Chinese Broadcast News

Subword Latent Semantic Analysis for Texttiling-Based Automa...

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International Symposium on Chinese Spoken language processing

作者： Yulian Yang Lei Xie Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781424429424

This paper proposes to perform latent semantic analysis (LSA) on character/syllable n-gram sequences of automatic speech recognition (ASR) transcripts, namely subword LSA, as an extension of our previous work on subword text tiling for automatic story segmentation of Chinese broadcast news. LSA represents the 'meaning' of a lexical term by a feature vector conveying the term's relations with other terms. We apply subword LSA vectors to the measurement of inter-sentence lexical score in text tiling-based story segmentation. Subword n-grams are robust to speech recognition errors, especially out-of-vocabulary (OOV) words, in lexical matching on Chinese ASR transcripts. This work combines the concept matching merit of LSA and the robustness of subwords. Experimental results on the TDT2 Mandarin corpus show that subword-LSA-based text tiling can effectively improve the story segmentation performance. Character-bigram-LSA-based text tiling achieves the best Fl-measure of 0.6598 with relative improvement of 17.4% over the conventional word-based text tiling and 6.5% over our previous syllable-bigram-based text tiling.

关键词： Broadcasting speech recognition speech analysis Automatic speech recognition Streaming media Vocabulary Performance analysis Natural languages Robustness Indexing

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Sequential system combination for machine translation of speech

Sequential system combination for machine translation of spe...

引用

IEEE Spoken language Technology Workshop

作者： Damianos Karakos Sanjeev Khudanpur Center for Language and Speech Processing and Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

System combination is a technique which has been shown to yield significant gains in speech recognition and machine translation. Most combination schemes perform an alignment between different system outputs in order to produce lattices (or confusion networks), from which a composite hypothesis is chosen, possibly with the help of a large language model. The benefit of this approach is two-fold: (i) whenever many systems agree with each other on a set of words, the combination output contains these words with high confidence; and (ii) whenever the systems disagree, the language model resolves the ambiguity based on the (probably correct) agreed upon context. The case of machine translation system combination is more challenging because of the different word orders of the translations: the alignment has to incorporate computationally expensive movements of word blocks. In this paper, we show how one can combine translation outputs efficiently, extending the incremental alignment procedure of (A-V.I. Rosti et al., 2008). A comparison between different system combination design choices is performed on an Arabic speech translation task.

关键词： speech processing speech recognition Automatic speech recognition Natural languages Lattices Context modeling Voting computer numerical control Contracts Sequences

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Pairwise document similarity in large collections with MapReduce 46

Pairwise document similarity in large collections with MapRe...

引用

46th Annual Meeting of the Association for Computational Linguistics: Human language Technologies, ACL 2008

作者： Elsayed, Tamer Lin, Jimmy Oard, Douglas W. Human Language Technology Center of Excellence UMIACS Laboratory for Computational Linguistics and Information Processing University of Maryland College ParkMD20742 United States Department of Computer Science The iSchool College of Information Studies

This paper presents a MapReduce algorithm for computing pairwise document similarity in large document collections. MapReduce is an attractive framework because it allows us to decompose the inner products involved in computing document similarity into separate multiplication and summation stages in a way that is well matched to efficient disk access patterns across several machines. On a collection consisting of approximately 900,000 newswire articles, our algorithm exhibits linear growth in running time and space in terms of the number of documents. © 2008 Association for Computational Linguistics.

关键词： MapReduce

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Pairwise document similarity in large collections with MapReduce

Pairwise document similarity in large collections with MapRe...

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46th Annual Meeting of the Association for Computational Linguistics: Human language Technologies, ACL-08: HLT

作者： Elsayed, Tamer Lin, Jimmy Oard, Douglas W. Department of Computer Science Human Language Technology Center of Excellenc University of Maryland College Park MD 20742 United States UMIACS Laboratory for Computational Linguistics and Information Processing College of Information Studies University of Maryland College Park MD 20742 United States

ISBN: (纸本)9781932432046

关键词： MapReduce

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Selecting Optimal Non-uniform Units for Hierarchical Unit Selection

Selecting Optimal Non-uniform Units for Hierarchical Unit Se...

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2008 International Conference on Audio，language and Image processing(2008国际声音、语言、图像过程大会)

作者： Jun Xu Dezhi Huang Haila Wang Lianhong Cai Yuan Dong Department of Computer Science and Technology Tsinghua University Beijing China Speech and Natural Language Processing Unit France Telecom R&D Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

For concatenative speech synthesis based on non-uniform unit selection, the key to improve the synthetic quality is the careful designing of measuring criteria respect to the units adopted. With our previous hierarchical non-uniform unit selection framework [1], two measurements for selecting optimal non-uniform units during searching at different layers are proposed in this paper, including inter-syllable pitch control and spectra distance by phonetic context. These measures are used as components of our cost function, especially for boundaries in front of syllables starting with voiceless consonants. Experiment shows it outperforms our previous system.

关键词： Distance measurement speech synthesis Engines Cost function speech Acoustic measurements speech processing

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A Lightweight Multiple String Matching Algorithm

A Lightweight Multiple String Matching Algorithm

引用

International Conference on computer science and Information Technology (CSIT)

作者： Liuling Dai Yuning Xia Beijing Laboratory of Intelligent Information Technology School of Computer Science BeiJing Institute of Technology Beijing China Center for Speech and Language Technologies RIIT Tsinghua University Beijing China

String matching is a fundamental issue in computer science. This paper presents a lightweight string matching algorithm for short pattern matching, in which less than 20 keywords are often involved in the pattern set. The new algorithm makes use of condensed hash tables and computes the shift distance after each test by observing the character that immediately passes the test window. Experiments show that the new algorithm improves execution speed and decreases memory requirement. This algorithm is suitable for applications with small pattern set (i.e. containing up to 30 keywords), particularly for embedded equipments.

关键词： Algorithm design and analysis Distance measurement Pattern matching computer science Memory management Classification algorithms Personal digital assistants

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