<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It is a difﬁcult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi <italic>et al.</italic>, (2006), and it is a modiﬁed version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefﬁcients of an innovation adaptive ﬁlter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics deﬁned on the mel spectrum determined from the reﬂection coefﬁcients. The paper presents the structure of the algorithm, deﬁnes its properties, lists parameter values, describes detection efﬁciency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.</p></abstract>

Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It is a difﬁcult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006), and it is a modiﬁed version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefﬁcients of an innovation adaptive ﬁlter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics deﬁned on the mel spectrum determined from the reﬂection coefﬁcients. The paper presents the structure of the algorithm, deﬁnes its properties, lists parameter values, describes detection efﬁciency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

Automatic speech signal segmentation based on the innovation adaptive filter

Faculty of Electronics Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Automatic speech signal segmentation based on the innovation adaptive filter"}

Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It...

Automatic speech signal segmentation based on the innovation adaptive filter

Published Online: Jun 26, 2014

Page range: 259 - 270

Received: Jan 21, 2013

DOI: https://doi.org/10.2478/amcs-2014-0019

Keywords
automatic speech segmentation, inter-phoneme boundaries, Schur adaptive ﬁltering, detection threshold determination

© 2014 Ryszard Makowski and Robert Hossa

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Automatic speech signal segmentation based on the innovation adaptive filter

Published Online: Jun 26, 2014

Page range: 259 - 270

Received: Jan 21, 2013

DOI: https://doi.org/10.2478/amcs-2014-0019

Keywordsautomatic speech segmentation, inter-phoneme boundaries, Schur adaptive ﬁltering, detection threshold determination

© 2014 Ryszard Makowski and Robert Hossa

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
automatic speech segmentation, inter-phoneme boundaries, Schur adaptive ﬁltering, detection threshold determination