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Advances in regional accent clustering in Swedish
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-3323-5311
2005 (English)In: Proceedings of European Conference on Speech Communication and Technology (Eurospeech), 2005, 2841-2844 p.Conference paper, Published paper (Refereed)
Abstract [en]

The regional pronunciation variation in Swedish is analysed on a large database. Statistics over each phoneme and for each region of Sweden are computed using the EM algorithm in a hidden Markov model framework to overcome the difficulties of transcribing the whole set of data at the phonetic level. The model representations obtained this way are compared using a distance measure in the space spanned by the model parameters, and hierarchical clustering. The regional variants of each phoneme may group with those of any other phoneme, on the basis of their acoustic properties. The log likelihood of the data given the model is shown to display interesting properties regarding the choice of number of clusters, given a particular level of details. Discriminative analysis is used to find the parameters that most contribute to the separation between groups, adding an interpretative value to the discussion. Finally a number of examples are given on some of the phenomena that are revealed by examining the clustering tree.

Place, publisher, year, edition, pages
2005. 2841-2844 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-6154Scopus ID: 2-s2.0-33745225165OAI: oai:DiVA.org:kth-6154DiVA: diva2:10783
Conference
Interspeech'2005 - Eurospeech Lisbon, Portugal September 4-8, 2005
Note
QC 20100630Available from: 2006-09-21 Created: 2006-09-21 Last updated: 2010-06-30Bibliographically approved
In thesis
1. Mining Speech Sounds: Machine Learning Methods for Automatic Speech Recognition and Analysis
Open this publication in new window or tab >>Mining Speech Sounds: Machine Learning Methods for Automatic Speech Recognition and Analysis
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis collects studies on machine learning methods applied to speech technology and speech research problems. The six research papers included in this thesis are organised in three main areas.

The first group of studies were carried out within the European project Synface. The aim was to develop a low latency phonetic recogniser to drive the articulatory movements of a computer generated virtual face from the acoustic speech signal. The visual information provided by the face is used as hearing aid for persons using the telephone.

Paper A compares two solutions to the problem of mapping acoustic to visual information that are based on regression and classification techniques. Recurrent Neural Networks are used to perform regression while Hidden Markov Models are used for the classification task. In the second case the visual information needed to drive the synthetic face is obtained by interpolation between target values for each acoustic class. The evaluation is based on listening tests with hearing impaired subjects were the intelligibility of sentence material is compared in different conditions: audio alone, audio and natural face, audio and synthetic face driven by the different methods.

Paper B analyses the behaviour, in low latency conditions, of a phonetic recogniser based on a hybrid of Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). The focus is on the interaction between the time evolution model learnt by the RNNs and the one imposed by the HMMs.

Paper C investigates the possibility of using the entropy of the posterior probabilities estimated by a phoneme classification neural network, as a feature for phonetic boundary detection. The entropy and its time evolution are analysed with respect to the identity of the phonetic segment and the distance from a reference phonetic boundary.

In the second group of studies, the aim was to provide tools for analysing large amount of speech data in order to study geographical variations in pronunciation (accent analysis).

Paper D and Paper E use Hidden Markov Models and Agglomerative Hierarchical Clustering to analyse a data set of about 100 millions data points (5000 speakers, 270 hours of speech recordings). In Paper E, Linear Discriminant Analysis was used to determine the features that most concisely describe the groupings obtained with the clustering procedure.

The third group belongs to studies carried out during the international project MILLE (Modelling Language Learning) that aims at investigating and modelling the language acquisition process in infants.

Paper F proposes the use of an incremental form of Model Based Clustering to describe the unsupervised emergence of phonetic classes in the first stages of language acquisition. The experiments were carried out on child-directed speech expressly collected for the purposes of the project

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. xix, 87 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2006:12
Keyword
speech, machine learning, data mining, signal processing
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-4111 (URN)91-7178-446-2 (ISBN)
Public defence
2006-10-06, F3, Sing Sing, Lindstedtsvägen 26, Stockholm, 13:00
Opponent
Supervisors
Note
QC 20100630Available from: 2006-09-21 Created: 2006-09-21 Last updated: 2010-06-30Bibliographically approved

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Salvi, Giampiero

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
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  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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