Locally learning heterogeneous manifolds for phonetic classification

Huang, Heyun; Liu, Yang; Bosch, L.F.M. ten; Cranen, B.; Boves, L.W.J.

doi:10.1016/j.csl.2015.12.002

Cited by 6 publications

(12 citation statements)

References 59 publications

(120 reference statements)

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“…In contrast, the linear model described in [11]) is one of few examples which attempt to exploit the varying local structure offered by a manifold.…”

Section: Topological Manifoldsmentioning

confidence: 99%

“…We explored the use of different definitions of BPCs. As the base, the phones are grouped into the 8 non-overlapping BPCs from [11]. These correspond to BPCs Q1-Q8 in the upper part of Table 1.…”

Section: A Non-linear Manifold Using Broad Phone Class Dnnsmentioning

confidence: 99%

“…A two-level linear computational model that is motivated by these considerations is presented in [11]. The first level comprises a set of discriminative linear transforms W T j , one for each of a set of overlapping BPCs Qj, j = 1, ..., N , that are used for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

“…An acoustic feature vector t is transformed using each W T j to obtain tj = W T j t and k-nearest neighbour methods are used to estimate p(Qj|tj) and p(c|Qj, tj) for each specific phone class c. These probabilities are combined in the second level to estimate the posterior probabilities p(c|tj) and hence to classify t. In acoustic feature vector phone classification experiments on TIMIT [12], the two-level linear classifier obtained slightly better results when BPC-specific linear transforms were learned, compared to a single transform. The authors of [11] speculate that better performance would be achieved using non-linear DNN-based transformations.…”

Section: Introductionmentioning

confidence: 99%

“…Our objective is to determine whether it is advantageous for phone-classification of feature vectors to treat the acoustic space A as a non-linear manifold, in which several BPC-dependent DNNs rather than a single DNN are used for phone classification. We use the phone classes from [11]. For a broad class Qj (j = 1, ..., N ), containing Kj phones, we train a DNN Dj to map an element a ∈ A onto a Kj + 1 dimensional vector Pj of posterior probabilities, where Pj(i) is the probability of phone i given a (i = 1, ..., Kj) and Pj(Kj + 1) is the probability that a corresponds to a phone outside class Qj.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Phone Classification Using a Non-Linear Manifold with Broad Phone Class Dependent DNNs

Bai¹,

Jančovič²,

Russell³

et al. 2017

Interspeech 2017

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Most state-of-the-art automatic speech recognition (ASR) systems use a single deep neural network (DNN) to map the acoustic space to the decision space. However, different phonetic classes employ different production mechanisms and are best described by different types of features. Hence it may be advantageous to replace this single DNN with several phone class dependent DNNs. The appropriate mathematical formalism for this is a manifold. This paper assesses the use of a nonlinear manifold structure with multiple DNNs for phone classification. The system has two levels. The first comprises a set of broad phone class (BPC) dependent DNN-based mappings and the second level is a fusion network. Various ways of designing and training the networks in both levels are assessed, including varying the size of hidden layers, the use of the bottleneck or softmax outputs as input to the fusion network, and the use of different broad class definitions. Phone classification experiments are performed on TIMIT. The results show that using the BPC-dependent DNNs provides small but significant improvements in phone classification accuracy relative to a single global DNN. The paper concludes with visualisations of the structures learned by the local and global DNNs and discussion of their interpretations.

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“…In contrast, the linear model described in [11]) is one of few examples which attempt to exploit the varying local structure offered by a manifold.…”

Section: Topological Manifoldsmentioning

confidence: 99%

Section: A Non-linear Manifold Using Broad Phone Class Dnnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Phone Classification Using a Non-Linear Manifold with Broad Phone Class Dependent DNNs

Bai¹,

Jančovič²,

Russell³

et al. 2017

Interspeech 2017

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Human disease MiRNA inference by combining target information based on heterogeneous manifolds

Ding

Luo

Liang

et al. 2018

Journal of Biomedical Informatics

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The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omics data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM.

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Computing low-dimensional representations of speech from socio-auditory structures for phonetic analyses

Plummer

Reidy

2018

Journal of Phonetics

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Low-dimensional representations of speech data, such as formant values extracted by linear predictive coding analysis or spectral moments computed from whole spectra viewed as probability distributions, have been instrumental in both phonetic and phonological analyses over the last few decades. In this paper, we present a framework for computing low-dimensional representations of speech data based on two assumptions: that speech data represented in high-dimensional data spaces lie on shapes called manifolds that can be used to map speech data to low-dimensional coordinate spaces, and that manifolds underlying speech data are generated from a combination of language-specific lexical, phonological, and phonetic information as well as culture-specific socio-indexical information that is expressed by talkers of a given speech community. We demonstrate the basic mechanics of the framework by carrying out an analysis of children’s productions of sibilant fricatives relative to those of adults in their speech community using the phoneigen package – a publicly available implementation of the framework. We focus the demonstration on enumerating the steps for constructing manifolds from data and then using them to map the data to a low-dimensional space, explicating how manifold structure affects the learned low-dimensional representations, and comparing the use of these representations against standard acoustic features in a phonetic analysis. We conclude with a discussion of the framework’s underlying assumptions, its broader modeling potential, and its position relative to recent advances in the field of representation learning.

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Locally learning heterogeneous manifolds for phonetic classification

Cited by 6 publications

References 59 publications

Phone Classification Using a Non-Linear Manifold with Broad Phone Class Dependent DNNs

Phone Classification Using a Non-Linear Manifold with Broad Phone Class Dependent DNNs

Human disease MiRNA inference by combining target information based on heterogeneous manifolds

Computing low-dimensional representations of speech from socio-auditory structures for phonetic analyses

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