| Description |
1 online resource (x, 101 pages) |
| Series |
Synthesis lectures on speech and audio processing ; #3 |
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Synthesis lectures on speech and audio processing (Online) ; #3.
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| Contents |
Principles -- Introduction -- Motivation -- From LSA to LSM -- Organization -- Latent semantic mapping -- Co-occurrence matrix -- Vector representation -- Interpretation -- LSM feature space -- Closeness measures -- LSM framework extension -- Salient characteristics -- Computational effort -- Off-line cost -- Online cost -- Possible shortcuts -- Probabilistic extensions -- Dual probability model -- Probabilistic latent semantic analysis -- Inherent limitations -- Applications -- Junk e-mail filtering -- Conventional approaches -- LSM-based filtering -- Performance -- Semantic classification -- Underlying issues -- Semantic inference -- Caveats -- Language modeling -- N-gram limitations -- MultiSpan language modeling -- Smoothing -- Pronunciation modeling -- Grapheme-to-phoneme conversion -- Pronunciation by latent analogy -- Speaker verification -- The task -- LSM-based speaker verification -- TTS unit selection -- Concatenative synthesis -- LSM-based unit selection -- LSM-based boundary training -- Perspectives -- Discussion -- Inherent tradeoffs -- General applicability -- Conclusion -- Summary -- Perspectives |
| Summary |
Latent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring "noise." This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This approach exhibits three main characteristics: 1) discrete entities (words and documents) are mapped onto a continuous vector space; 2) this mapping is determined by global correlation patterns; and 3) dimensionality reduction is an integral part of the process. Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome (LSM) is a data-driven framework for modeling meaningful global relationships implicit in large volumes of (not necessarily textual) data. This monograph gives a general overview of the framework, and underscores the multifaceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent tradeoffs associated with the approach, and some perspectives on its general applicability to data-driven information extraction |
| Notes |
Title from PDF title page (viewed September 13, 2007) |
| Bibliography |
Includes bibliographical references (pages 89-100) |
| Notes |
English |
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Print version record |
| Subject |
Latent semantic indexing.
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Semantics -- Data processing.
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Semantics -- Mathematical models.
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Computational linguistics.
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Automatic speech recognition.
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computational linguistics.
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LANGUAGE ARTS & DISCIPLINES -- Linguistics -- Psycholinguistics.
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Automatic speech recognition
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Computational linguistics
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Latent semantic indexing
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Semantics -- Data processing
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Semantics -- Mathematical models
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| Form |
Electronic book
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| ISBN |
159829105X |
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9781598291056 |
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9781598294033 |
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1598294032 |
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9783031025563 |
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3031025563 |
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