A dynamic alignment algorithm for imperfect speech and transcript

This paper presents a novel alignment approach for imperfect speech and the corresponding transcription. The algorithm gets started with multi-stage sentence boundary detection in audio, followed by a dynamic programming based search, to find the optimal alignment and detect the mismatches at sentence level. Experiments show promising performance, compared to the traditional forced alignment approach. The proposed algorithm has already been applied in preparing multimedia content for an online English training platform.

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A unified alignment algorithm for bilingual data

Natural Language Engineering ◽

10.1017/s135132491100026x ◽

2011 ◽

Vol 19 (1) ◽

pp. 33-60 ◽

Cited By ~ 3

Author(s):

CHRISTOPH TILLMANN ◽

SANJIKA HEWAVITHARANA

Keyword(s):

Dynamic Programming ◽

Speech Recognition ◽

Search Algorithm ◽

Alignment Algorithm ◽

Comparable Data ◽

Beam Search ◽

Sentence Level ◽

Unified Algorithm ◽

The One ◽

Document Level

AbstractThe paper presents a novel unified algorithm for aligning sentences with their translations in bilingual data. With the help of ideas from a stack-based dynamic programming decoder for speech recognition (Ney 1984), the search is parametrized in a novel way such that the unified algorithm can be used on various types of data that have been previously handled by separate implementations: the extracted text chunk pairs can be either sub-sentential pairs, one-to-one, or many-to-many sentence-level pairs. The one-stage search algorithm is carried out in a single run over the data. Its memory requirements are independent of the length of the source document, and it is applicable to sentence-level parallel as well as comparable data. With the help of a unified beam-search candidate pruning, the algorithm is very efficient: it avoids any document-level pre-filtering and uses less restrictive sentence-level filtering. Results are presented on a Russian–English, a Spanish–English, and an Arabic–English extraction task. Based on simple word-based scoring features, text chunk pairs are extracted out of several trillion candidates, where the search is carried out on 300 processors in parallel.

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Fast DNA Sequence Alignment Algorithm Based on Quality Score Using Improved Dynamic Programming and Fuzzy Gap Cost Control

Current Bioinformatics ◽

10.2174/1574893609666140523000227 ◽

2014 ◽

Vol 9 (5) ◽

pp. 540-547

Author(s):

Kwang Kim ◽

Hyun Park ◽

Doo Song

Keyword(s):

Dynamic Programming ◽

Dna Sequence ◽

Sequence Alignment ◽

Cost Control ◽

Quality Score ◽

Alignment Algorithm ◽

Sequence Alignment Algorithm ◽

Dna Sequence Alignment ◽

Improved Dynamic Programming

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Multi-dimension Multi-stage Fuzzy Optimum Dynamic Programming Method with Complicated Information

2009 International Conference on Scalable Computing and Communications; Eighth International Conference on Embedded Computing ◽

10.1109/embeddedcom-scalcom.2009.31 ◽

2009 ◽

Author(s):

Yingwei Jin ◽

Yanming Shen

Keyword(s):

Dynamic Programming ◽

Programming Method ◽

Dynamic Programming Method ◽

Multi Stage

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Dynamic programming in parallel boundary detection with application to ultrasound intima-media segmentation

Medical Image Analysis ◽

10.1016/j.media.2013.05.009 ◽

2013 ◽

Vol 17 (8) ◽

pp. 892-906 ◽

Cited By ~ 8

Author(s):

Yuan Zhou ◽

Xinyao Cheng ◽

Xiangyang Xu ◽

Enmin Song

Keyword(s):

Dynamic Programming ◽

Boundary Detection

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Towards Language-Independent Sentence Boundary Detection

Computational Linguistics and Intelligent Text Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-540-24630-5_17 ◽

2004 ◽

pp. 142-145

Author(s):

Do-Gil Lee ◽

Hae-Chang Rim

Keyword(s):

Boundary Detection ◽

Sentence Boundary

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Lane Recognition Method Using Multi-stage Dynamic Programming

Journal of Mechanical Engineering ◽

10.3901/jme.2011.08.141 ◽

2011 ◽

Vol 47 (08) ◽

pp. 141

Author(s):

Dezhi GAO

Keyword(s):

Dynamic Programming ◽

Recognition Method ◽

Multi Stage ◽

Lane Recognition

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Deep Neural Network Based Sentence Boundary Detection and End Marker Suggestion for Social Media Text

2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) ◽

10.1109/icccis48478.2019.8974495 ◽

2019 ◽

Author(s):

Jagroop Kaur ◽

Jaswinder Singh

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Neural Network ◽

Boundary Detection ◽

Social Media Text ◽

Sentence Boundary

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An Introduction to Dynamic Programming

Journal of the Staple Inn Actuarial Society ◽

10.1017/s0020269x00007313 ◽

1961 ◽

Vol 16 (04) ◽

pp. 261-274

Author(s):

Brian Gluss

Keyword(s):

Dynamic Programming ◽

Small Body ◽

Decision Processes ◽

Theory And Practice ◽

Rand Corporation ◽

The Past ◽

Multi Stage ◽

The Subject ◽

New Research ◽

General Mathematical Theory

Dynamic programming, a mathematical field that has grown up in the past few years, is recognized in the U.S.A. as an important new research tool. However, in other countries, little interest has as yet been taken in the subject, nor has much research been performed. The objective of this paper is to give an expository introduction to the field, and give an indication of the variety of actual and possible areas of application, including actuarial theory.In the last decade a large amount of research has been performed by a small body of mathematicians, most of them members of the staff of the RAND Corporation, in the field of multi-stage decision processes, and during this time the theory and practice of the art have experienced great advances. The leading force in these advances has been Richard Bellman, whose contributions to the subject, which he has entitledDynamic Programming[1], have had effects not only in immediate fields of application but also in general mathematical theory; for example, the calculus of variations (see chapter IX of [1]), and linear programming (chapter VI).

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