International Conference on Speech and Computer

SPECOM 2015: Speech and Computer pp 161-169 | Cite as

Combining Prosodic and Lexical Classifiers for Two-Pass Punctuation Detection in a Russian ASR System

  • Olga Khomitsevich
  • Pavel Chistikov
  • Tatiana Krivosheeva
  • Natalia Epimakhova
  • Irina Chernykh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9319)

Abstract

We propose a system for automatic punctuation prediction in recognized speech using prosodic, word and grammatical features. An SVM classifier is trained using prosody, and a CRF classifier is trained on a large text dataset using word-based features. The probabilities are then fused to produce a joint decision on comma and period placement, with a second classification pass for question mark detection. Training two classifiers separately enables us to avoid data sparseness for the lexical classifier, and to increase the overall robustness of the system. This works well for Russian and could be applied to other inflected languages. The system was tested on different speech styles. On manual transcripts, we achieved an F-score of 50–71 % for periods, 46–66 % for commas, 19–47 % for question marks, and 77–87 % for “mark/no mark” classification. The results for recognizer output are 46–66 % for periods, 43–60 % for commas, 10–38 % for questions, and 64–80 % for “mark/no mark”.

Keywords

Punctuation prediction Sentence boundary detection Speech recognition Conditional Random Fields Support Vector Machine Russian 

Notes

Acknowledgements

The work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.579.21.0008, ID RFMEFI57914X0008, and by the Government of the Russian Federation, Grant 074-U01.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Olga Khomitsevich
    • 1
    • 2
  • Pavel Chistikov
    • 1
  • Tatiana Krivosheeva
    • 3
  • Natalia Epimakhova
    • 3
  • Irina Chernykh
    • 2
    • 3
  1. 1.Speech Technology CenterSaint-PetersburgRussia
  2. 2.ITMO UniversitySaint-PetersburgRussia
  3. 3.STC-Innovations LtdSaint-PetersburgRussia

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