The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Naomi Ogasawara - One of the best experts on this subject based on the ideXlab platform.
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Lexical Representation of Japanese vowel devoicing.
Language and speech, 2013Co-Authors: Naomi OgasawaraAbstract:Vowel devoicing happens in Japanese when the high vowel is between voiceless consonants. The aim of this study is to investigate the Lexical Representation of vowel devoicing. A long-term repetition-priming experiment was conducted. Participants shadowed words containing either a devoiced or a voiced vowel in three priming paradigms, and their shadow responses were analyzed. It was found that participants produced the phonologically appropriate allophone most of the time based on the consonantal environments. Shadowing latencies for the voiced stimuli were faster than for the devoiced stimuli in the environment where the vowel should be voiced; while, no significant RT difference was observed between the two forms in the environment where vowel devoicing was expected. In addition, a priming effect between the devoiced and voiced stimuli emerged only in the devoicing environment. The results suggest that since vowel devoicing is very common in spoken Japanese, the devoiced form may be stored in the lexicon. The results also suggest a link between the two forms in the lexicon and a direct access between an input and a Lexical Representation without going through intermediate levels that usually cost extra processes.
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Lexical Representation of Japanese vowel devoicing.
Language and Speech, 2012Co-Authors: Naomi OgasawaraAbstract:Vowel devoicing happens in Japanese when the high vowel is between voiceless consonants. The aim of this study is to investigate the Lexical Representation of vowel devoicing. A long-term repetitio...
Helen Meng - One of the best experts on this subject based on the ideXlab platform.
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a hierarchical Lexical Representation for bi directional spelling to pronunciation pronunciation to spelling generation
Speech Communication, 2001Co-Authors: Helen MengAbstract:Abstract We propose a hierarchical framework for integrating a variety of linguistic knowledge sources of Lexical Representation in English, in order to facilitate their concurrent utilization in language applications. Our unified Lexical Representation encompasses information including morphology, stress, syllabification, phonemics and graphemics. Each linguistic knowledge source occupies a distinct stratum in the hierarchy. The merits of the proposed framework is demonstrated on the test bed of bi-directional spelling-to-pronunciation/pronunciation-to-spelling generation. Constraints from the multiple linguistic knowledge sources are administered in parallel during generation, by means of a probabilistic parsing paradigm. This paper extends the previous work on spelling-to-pronunciation generation as reported in ( Meng et al., 1996 ), by presenting our full results on bi-directional generation which includes pronunciation-to-spelling generation. We will also introduce a robust parsing technique which is aimed for maximizing the coverage of our parser for generation. We believe that our formalism will be especially applicable for augmenting the vocabulary of existing speech recognition and synthesis systems. This work is also the precursor to the ANGIE system ( Lau and Seneff, 1997 ; Seneff et al., 1996 ), which extends our Lexical Representation to the phonetic level, and applies successfully in speech recognition, word spotting and durational modeling ( Chung and Seneff, 1997 ).
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A hierarchical Lexical Representation for bi-directional spelling-to-pronunciation/pronunciation-to-spelling generation
Speech Communication, 2001Co-Authors: Helen MengAbstract:Abstract We propose a hierarchical framework for integrating a variety of linguistic knowledge sources of Lexical Representation in English, in order to facilitate their concurrent utilization in language applications. Our unified Lexical Representation encompasses information including morphology, stress, syllabification, phonemics and graphemics. Each linguistic knowledge source occupies a distinct stratum in the hierarchy. The merits of the proposed framework is demonstrated on the test bed of bi-directional spelling-to-pronunciation/pronunciation-to-spelling generation. Constraints from the multiple linguistic knowledge sources are administered in parallel during generation, by means of a probabilistic parsing paradigm. This paper extends the previous work on spelling-to-pronunciation generation as reported in ( Meng et al., 1996 ), by presenting our full results on bi-directional generation which includes pronunciation-to-spelling generation. We will also introduce a robust parsing technique which is aimed for maximizing the coverage of our parser for generation. We believe that our formalism will be especially applicable for augmenting the vocabulary of existing speech recognition and synthesis systems. This work is also the precursor to the ANGIE system ( Lau and Seneff, 1997 ; Seneff et al., 1996 ), which extends our Lexical Representation to the phonetic level, and applies successfully in speech recognition, word spotting and durational modeling ( Chung and Seneff, 1997 ).
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A Hierarchical Lexical Representation for Pronunciation Generation
Data-Driven Techniques in Speech Synthesis, 2001Co-Authors: Helen MengAbstract:We propose a unified framework for integrating a variety of linguistic knowledge sources for representing the English word, to facilitate their concurrent utilization in language applications. Our hierarchical Lexical Representation encompasses information such as morphology, stress, syllabification, phonemics and graphemics. Each occupies a distinct stratum in the hierarchy, and the constraints they provide are administered in parallel during generation via a probabilistic parsing paradigm. The merits of the proposed methodology have been demonstrated on the test bed of bi-directional spelling-to-pronunciation/pronunciationto-spelling generation. This chapter focuses on the former task. Training and testing corpora are derived from the high-frequency portion of the Brown corpus (10,000 words), augmented with markers indicating stress and word morphology. The system was evaluated on an unseen test set, and achieved a parse coverage of 94%, with a word accuracy of 71.8% and a phoneme accuracy of 92.5% using a set of 52 phonemes. We have also conducted experiments to assess empirically (a) the relative contribution of each linguistic layer towards generation accuracy, and (b) the relative merits of the overall hierarchical design. We believe that our formalism will be especially applicable for augmenting the vocabulary of existing speech recognition and synthesis systems.
Ulrich Hans Frauenfelder - One of the best experts on this subject based on the ideXlab platform.
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Lexical Representation of phonological variants: Evidence from pseudohomophone effects in different regiolects
Journal of Memory and Language, 2011Co-Authors: Audrey Buerki, F.-xavier Alario, Ulrich Hans FrauenfelderAbstract:This study examined the Lexical Representation of words with two pronunciation variants. We tested whether both the schwa and reduced variants of French words are stored as Lexical entries. The results of four experiments in which speakers named pseudohomophones and pseudowords show an advantage for pseudohomophones over matched pseudowords for both variants. As the pseudohomophone effect is assumed to reflect the activation of phonologically matching stored phonological Representations, these results suggest that both variants of schwa words are stored. Importantly, the pseudohomophone effect is found for alternating words (Experiments 1 and 2) and for non-alternating words when the non-produced variant corresponds to the word's spelling (Experiment 3) or is frequently encountered in the speech of speakers of other regiolects (Experiment 4). These findings extend the scope of our previous proposal that words with two variants are stored with two lexemes. This conclusion needs to be integrated in word production models. (C) 2011 Elsevier Inc. All rights reserved.
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Lexical Representation of phonological variants: Evidence from pseudohomophone effects in different regiolects
Journal of Memory and Language, 2011Co-Authors: Audrey Bürki, F.-xavier Alario, Ulrich Hans FrauenfelderAbstract:This study examined the Lexical Representation of words with two pronunciation variants. We tested whether both the schwa and reduced variants of French words are stored as Lexical entries. The results of four experiments in which speakers named pseudohomophones and pseudowords show an advantage for pseudohomophones over matched pseudowords for both variants. As the pseudohomophone effect is assumed to reflect the activation of phonologically matching stored phonological Representations, these results suggest that both variants of schwa words are stored. Importantly, the pseudohomophone effect is found for alternating words (Experiments 1 and 2) and for non-alternating words when the non-produced variant corresponds to the word’s spelling (Experiment 3) or is frequently encountered in the speech of speakers of other regiolects (Experiment 4). These findings extend the scope of our previous proposal that words with two variants are stored with two lexemes. This conclusion needs to be integrated in word production models.
Cynthia M. Connine - One of the best experts on this subject based on the ideXlab platform.
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Lexical Representation of phonological variation in spoken word recognition
Journal of Memory and Language, 2007Co-Authors: Larissa J. Ranbom, Cynthia M. ConnineAbstract:There have been a number of mechanisms proposed to account for recognition of phonological variation in spoken language. Five of these mechanisms were considered here, including underspecification, inference, feature parsing, tolerance, and a frequency-based Representational account. A corpus analysis and five experiments using the nasal flap (found in a production of gentle in American English) both in isolation and in biasing sentential context failed to fully support any of these accounts. The results support a strong phonological Representation for the [nt] form and a gradient strength Representation in the lexicon for the nasal flap that is influenced by production frequency. The results are discussed in terms of orthographic and phonological experience with word forms in the formation of Lexical Representations.
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Phonological variation in spoken word recognition : Episodes and abstractions
The Linguistic Review, 2006Co-Authors: Cynthia M. Connine, Eleni PinnowAbstract:Phonological variation in spoken words is a ubiquitous aspect of spontaneous speech and presents a challenge for recognition of spoken words. We discuss two classes of models, abstract and episodic, that have been proposed for spoken word recognition. Abstract theories rely on inference processes and/or underspecified Representations to account for spoken word recognition. Episodic theories assume a Lexical Representation that encodes each spoken word event with exposure frequency linked to strength of a Lexical entry. A model is proposed that posits a frequency-driven phonological variant Lexical Representation. The model assumes that a word may have more than one variant Representation and that exposure to phonological variant form influences the strength of a given variant Representation. Evidence for the proposed model is reviewed for a number of variants (nasal flaps, schwa deletion and medial flaps).
Emre Kiciman - One of the best experts on this subject based on the ideXlab platform.
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sparse Lexical Representation for semantic entity resolution
International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yuzhe Jin, Kuansan Wang, Emre KicimanAbstract:This paper addresses the problem of semantic entity resolution (SER), which aims to determine whether some or none of the entities in a knowledge base is mentioned in a given web document. The Lexical features, e.g., words and phrases, which are critical to the resolution of the semantic entities are typically of a small amount compared to all Lexical features in the web document, and therefore can be modeled as sparse signals. Two techniques leveraging the principles of sparse signal recovery are proposed to identify the sparse, salient Lexical features: one technique, based on the Lasso algorithm with the l2-norm distance metric, attempts to recover all the salient Lexical features at once; the other technique, namely Posterior Probability Pursuit (PPP), sequentially identifies salient features one after one using the negative log posterior probability as the distance metric. Using a knowledge base consisting of about 100 million entities, we show that the proposed techniques exploiting the sparsity nature underlying SER deliver substantial performance improvement over baseline methods without sparsity consideration, demonstrating the potentials of sparse signal techniques in entity-centric web information processing.
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ICASSP - Sparse Lexical Representation for semantic entity resolution
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yuzhe Jin, Kuansan Wang, Emre KicimanAbstract:This paper addresses the problem of semantic entity resolution (SER), which aims to determine whether some or none of the entities in a knowledge base is mentioned in a given web document. The Lexical features, e.g., words and phrases, which are critical to the resolution of the semantic entities are typically of a small amount compared to all Lexical features in the web document, and therefore can be modeled as sparse signals. Two techniques leveraging the principles of sparse signal recovery are proposed to identify the sparse, salient Lexical features: one technique, based on the Lasso algorithm with the l2-norm distance metric, attempts to recover all the salient Lexical features at once; the other technique, namely Posterior Probability Pursuit (PPP), sequentially identifies salient features one after one using the negative log posterior probability as the distance metric. Using a knowledge base consisting of about 100 million entities, we show that the proposed techniques exploiting the sparsity nature underlying SER deliver substantial performance improvement over baseline methods without sparsity consideration, demonstrating the potentials of sparse signal techniques in entity-centric web information processing.