Evaluating the effect of using different transcription schemes in building a speech recognition system for Arabic

Abstract

It is well-known that the Arabic language poses non-trivial issues for Automatic Speech Recognition (ASR) systems. This paper is concerned with the problems posed by the complex morphology of the language and the absence of diacritics in the written form of the language. Several acoustic and language models are built using different transcription resources, namely a grapheme-based transcription which uses non-diacriticised text materials, phoneme-based transcriptions obtained from automatic diacritisation tools (SAMA or MADAMIRA), and a predefined dictionary. The paper presents a comprehensive assessment for the aforementioned transcription schemes by employing them in building a collection of Arabic ASR systems using the GALE (phase 3) Arabic broadcast news and broadcast conversational speech datasets LDC (2015), which include 260 h of recorded material. Contrary to our expectations, the experimental evidence confirms that the use of grapheme-based transcription is superior to the use of phoneme-based transcription. To investigate this further, several modifications are applied to the MADAMIRA analysis by applying a number of simple phonological rules. These improvements have a substantial effect on the systems’ performance, but it is still inferior to the use of a simple grapheme-based transcription. The research also examined the use of a manually diacriticised subset of the data in training the ASR system and compared it with the use of grapheme-based transcription and phoneme-based transcription obtained from MADAMIRA. The goal of this step is to validate MADAMIRA’s analysis. The results show that using the manually diacriticised text in generating the phonetic transcription can significantly decrease the WER compared to the use of MADAMIRA diacriticised text and also the isolated graphemes. The results obtained strongly indicate that providing the training model with less information about the data (only graphemes) is less damaging than providing it with inaccurate information.

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Notes

  1. 1.

    Diacritics are mainly markers for both vowels and consonants. These markers include short vowels, dagger Alif, sukun, nunation, and gemination marker.

  2. 2.

    GALE (phase 3) Arabic broadcast News and Broadcast Conversation datasets were developed by the Linguistic Data Consortium (LDC). Obtaining these datasets is exclusively available to LDC members through their website.

  3. 3.

    SAMA is a software tool for the morphological analysis of Standard Arabic. It is an updated version of Buckwalter Arabic Morphological Analyser (BAMA). SAMA analyses each Arabic word token by providing all possible prefix-stem-suffix segmentations, and lists all possible annotation solutions, with assignment of all diacritic marks, morpheme boundaries, and all Part-of-Speech (POS) tags. The choice is then left to users to select the most appropriate annotation among the generated output. Accessing this tool is exclusively available to LDC members through this link: https://catalog.ldc.upenn.edu/LDC2010L01

  4. 4.

    MADAMIRA is a toolkit that provides linguistic information such as tokenisation, lemmatisation, diacritisation, and part of speech tagging. It contains models for both MSA and Egyptian. What sets MADAMIRA apart from similar tools is that it takes word context into account, which makes the generated analysis more accurate. Non-commercial license is freely available at: http://innovation.columbia.edu/technologies/CU14012

  5. 5.

    http://htk.eng.cam.ac.uk

  6. 6.

    https://kaldi-asr.org

  7. 7.

    http://www.speech.cs.cmu.edu

  8. 8.

    MADAMIRA requires a SAMA-style set of prefix-, stem- and suffix-dictionaries: we used it with the standard SAMA dictionaries with some extensions as discussed here.

  9. 9.

    http://alt.qcri.org/resources/speech/dictionary/arar_lexicon_2014-03-17.txt.bz2

  10. 10.

    This is particularly challenging when these characters appear adjacent to one another, since you need to know whether the first is a consonant or a vowel before you can decide which class the second belongs to; but in order to know whether the first is a vowel or a consonant you need to know which class the second belongs to.

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Correspondence to Eiman Alsharhan.

Appendix

Appendix

The research uses Buckwalter scheme to romanise Arabic characters in all textual resources, as Arabic characters are not acceptable by HTK. The research also uses SAMPA transcription scheme to represent the phonetic notation in the dictionary. Details are given in the table below. Entries in bold indicate that amendment was applied to the original form.

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Alsharhan, E., Ramsay, A. & Ahmed, H. Evaluating the effect of using different transcription schemes in building a speech recognition system for Arabic. Int J Speech Technol (2020). https://doi.org/10.1007/s10772-020-09720-z

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Keywords

  • Natural language processing
  • Arabic speech recognition
  • Diacritisation
  • Phonetic transcription
  • MADAMIRA
  • SAMA
  • Phonological rules