19 research outputs found

    Phonetic and Phonological Posterior Search Space Hashing Exploiting Class-Specific Sparsity Structures

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    This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors' intrinsic sparsity structures. The posterior probabilities are estimated for phonetic and phonological classes using deep neural network (DNN) computational framework. Exploiting the class-specific sparsity leads to a simple quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small number of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of neighboring exemplars. The kk nearest neighbor (kkNN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phonetic classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of kkNN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

    Phonetic and Phonological Posterior Search Space Hashing Exploiting Class-Specific Sparsity Structures

    Get PDF
    This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors' intrinsic sparsity structures. The posterior probabilities are estimated for phonetic and phonological classes using deep neural network (DNN) computational framework. Exploiting the class-specific sparsity leads to a simple quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small number of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of neighboring exemplars. The kk nearest neighbor (kkNN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phonetic classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of kkNN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

    Efficient Posterior Exemplar Search Space Hashing Exploiting Class-Specific Sparsity Structures

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    This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors' intrinsic sparsity structures. The posterior probabilities are estimated for phonetic and phonological classes using deep neural network (DNN) computational framework. Exploiting the class-specific sparsity leads to a simple quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small subset of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of similar exemplars. The kk nearest neighbor (kkNN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phoneme classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of kkNN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

    Redundant Hash Addressing for Large-Scale Query by Example Spoken Query Detection

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    State of the art query by example spoken term detection (QbE-STD) systems rely on representation of speech in terms of sequences of class-conditional posterior probabilities estimated by deep neural network (DNN). The posteriors are often used for pattern matching or dynamic time warping (DTW). Exploiting posterior probabilities as speech representation propounds diverse advantages in a classification system. One key property of the posterior representations is that they admit a highly effective hashing strategy that enables indexing the large archive in divisions for reducing the search complexity. Moreover, posterior indexing leads to a compressed representation and enables pronunciation dewarping and partial detection with no need for DTW. We exploit these characteristics of the posterior space in the context of redundant hash addressing for query-by-example spoken term detection (QbE-STD). We evaluate the QbE-STD system on AMI corpus and demonstrate that tremendous speedup and superior accuracy is achieved compared to the state-of-the-art pattern matching and DTW solutions. The system has great potential to enable massively large scale query detection

    Sound Pattern Matching for Automatic Prosodic Event Detection

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    Prosody in speech is manifested by variations of loudness, exaggeration of pitch, and specific phonetic variations of prosodic segments. For example, in the stressed and unstressed syllables, there are differences in place or manner of articulation, vowels in unstressed syllables may have a more central articulation, and vowel reduction may occur when a vowel changes from a stressed to an unstressed position. In this paper, we characterize the sound patterns using phonological posteriors to capture the phonetic variations in a concise manner. The phonological posteriors quantify the posterior probabilities of the phonological features given the input speech acoustics, and they are obtained using the deep neural network (DNN) computational method. Built on the assumption that there are unique sound patterns in different prosodic segments, we devise a sound pattern matching (SPM) method based on 1-nearest neighbour classifier. In this work, we focus on automatic detection of prosodic stress placed on words, called also emphasized words. We evaluate the SPM method on English and French data with emphasized words. The word emphasis detection works very well also on cross-lingual tests, that is using a French classifier on English data, and vice versa

    Low-Rank Representation For Enhanced Deep Neural Network Acoustic Models

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    Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DNN) to estimate class-conditional posterior probabilities. The posterior probabilities are used for acoustic modeling in hidden Markov models (HMM), and form a hybrid DNN-HMM which is now the leading edge approach to solve ASR problems. The present work builds upon the hypothesis that the optimal acoustic models are sparse and lie on multiple low-rank probability subspaces. Hence, the main goal of this Master project aimed at investigating different ways to restructure the DNN outputs using low-rank representation. Exploiting a large number of training posterior vectors, the underlying low-dimensional subspace can be identified, and low-rank decomposition enables separation of the “optimal” posteriors from the spurious (unstructured) uncertainties at the DNN output. Experiments demonstrate that low-rank representation can enhance posterior probability estimation, and lead to higher ASR accuracy. The posteriors are grouped according to their subspace similarities, and structured through low-rank decomposition. Furthermore, a novel hashing technique is proposed exploiting the low-rank property of posterior subspaces that enables fast search in the space of posterior exemplars

    Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

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    Peer reviewe

    Learning words and syntactic cues in highly ambiguous contexts

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    The cross-situational word learning paradigm argues that word meanings can be approximated by word-object associations, computed from co-occurrence statistics between words and entities in the world. Lexicon acquisition involves simultaneously guessing (1) which objects are being talked about (the ”meaning”) and (2) which words relate to those objects. However, most modeling work focuses on acquiring meanings for isolated words, largely neglecting relationships between words or physical entities, which can play an important role in learning. Semantic parsing, on the other hand, aims to learn a mapping between entire utterances and compositional meaning representations where such relations are central. The focus is the mapping between meaning and words, while utterance meanings are treated as observed quantities. Here, we extend the joint inference problem of word learning to account for compositional meanings by incorporating a semantic parsing model for relating utterances to non-linguistic context. Integrating semantic parsing and word learning permits us to explore the impact of word-word and concept-concept relations. The result is a joint-inference problem inherited from the word learning setting where we must simultaneously learn utterance-level and individual word meanings, only now we also contend with the many possible relationships between concepts in the meaning and words in the sentence. To simplify design, we factorize the model into separate modules, one for each of the world, the meaning, and the words, and merge them into a single synchronous grammar for joint inference. There are three main contributions. First, we introduce a novel word learning model and accompanying semantic parser. Second, we produce a corpus which allows us to demonstrate the importance of structure in word learning. Finally, we also present a number of technical innovations required for implementing such a model

    Proceedings of the 7th Sound and Music Computing Conference

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    Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010
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