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Pronunciation Modeling for Dialectal Arabic Speech Recognition Hassan Al-Haj, Roger Hsiao, Ian Lane, Alan W

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Pronunciation Modeling for Dialectal Arabic Speech Recognition Hassan Al-Haj, Roger Hsiao, Ian Lane, Alan W Pronunciation Modeling for Dialectal Arabic Speech Recognition Hassan Al-Haj, Roger Hsiao, Ian Lane, Alan W. Black, Alex Waibel interACT / Language Technologies Institute School of Computer Science, Carnegie Mellon University Pittsburgh, PA 15213, USA {hhaj,wrhsiao,ianlane,awb,ahw}@cs.cmu.edu Abstract— Short vowels in Arabic are normally omitted in TABLE I written text which leads to ambiguity in the pronunciation. This EXAMPLES OF ARABIC WORDS AND THEIR VOWELIZED FORMS is even more pronounced for dialectal Arabic where a single Written Pronunciation Meaning word can be pronounced quite differently based on the speaker’s Form nationality, level of education, social class and religion. In this /ktb/ (MSA) paper we focus on pronunciation modeling for Iraqi-Arabic he wrote speech. We introduce multiple pronunciations into the Iraqi /kataba/ speech recognition lexicon, and compare the performance, when it was written weights computed via forced alignment are assigned to the /kutiba/ different pronunciations of a word. Incorporating multiple make/cause him write pronunciations improved recognition accuracy compared to a /kattab/ single pronunciation baseline and introducing pronunciation books /kutub/ weights further improved performance. Using these techniques books (indefinite) an absolute reduction in word-error-rate of 2.4% was obtained /kutubin/ compared to the baseline system. /mdrbjn/ (Iraqi) trained (adj. , dialect 1) I. INTRODUCTION /mudarrabijn/ The Arabic alphabet only consists of letters for long vowels trained (adj. , dialect 2) and consonants. Other pronunciation phenomena, including /mdarrabijn/ two trainers short vowels (harakat), nunation (tanwin) and consonant /mdarbijn/ doubling (shadda), are not typically written. However, they trainers can be explicitly indicated using diacritics. Vowel diacritics /mudarribijn/ represent the three short vowels: a, i, and u (fatha, kasra and damma) or the absence of a vowel (sukun). For example, the First, the vowelized form of a word is ambiguous, thus it must either be explicitly hypothesized, or this ambiguity must ب four vowel diacritics in conjunction with the Arabic letter /b/1 are written as: be handled in the models applied during ASR decoding. ba/ (fatha) Second, the absence of short vowels increases ambiguity in/ بَ bi/ (kasra) the language model. Words with identical written-forms may/ ِ ب bu/ (damma) actually be homographs that occur in very different linguistic/ بُ b/ (sukun), absence of a vowel. contexts. Treating them identically will decrease the/ بْ Diacritics indicating nunation (tanwin) only occur in word predictability of the language model. Table I shows there are final positions in indefinite nominals (nouns, adjectives and six possible interpretations of the modern standard Arabic adverbs). They indicate a short vowel followed by an (MSA) word /ktb/ showing the difficulty of this task. bin/. The diacritic There exist many dialects of Arabic which are used in daily/ بٍ ,/bun/ بٌ ,/unwritten n sound: ً /ban bb/. Shadda can communication. These dialects differ significantly from MSA/ بّ :shadda indicates a consonant doubling which is primary used in written text and formal ب bbu/ or/ ب :also combine with a vowel or a nunation as in /bbun/. In this paper, a word fully annotated with diacritics, as communications. As opposed to MSA, dialectal Arabic can be it will be pronounced, will be called the “vowelized form”. spoken quite differently based on a person’s nationality, level Diacritics are normally omitted (or appear only partially) in of education, social class and religion. This creates numerous most written text. This leads to two main problems when pronunciations of the same word, for example, the four developing Arabic ASR systems. possible pronunciations of the Iraqi word /mdrbjn/ as given in Table I. 1 We use the Buckwalter transliteration to romanize Arabic examples (Buckwalter, 2002). U.S. Government work not protected by U.S. copyright 525 ASRU 2009 Resources such as morphological analysers, which are II. PRONUNCIATION MODELLING FOR IRAQI-ARABIC generally used when building MSA speech recognition We built and compared three vowelized Iraqi speech systems, are not available for resource deficient Arabic recognition systems. The first uses a single pronunciation per dialects. Moreover, it is non-trivial to automatically generate word, while the other two use multiple pronunciations. The diacritics using basic grammatical rules due to the large two multi-pronunciations systems use the same recognition variability of pronunciations within Arabic dialects. In this lexicon but differ in the probabilities (weights) assigned to the work we investigate explicitly modeling diacritics in the pronunciations variants of a word. acoustic model, and handling pronunciation variants within the recognition dictionary. We built and compared three A. Vowelized Pronunciation Dictionaries vowelized ASR systems: a baseline system, where each The single pronunciation dictionary contains 130k words. lexical entry has only a single pronunciation; a multi- The pronunciations for 95k of the words were obtained from pronunciation system, where each lexical entry could contain manually compiled pronunciation dictionaries, including the more than one pronunciation, and a second multi- LDC Iraqi-Arabic Morphological Lexicon, as well as, pronunciation system where entries in the recognition wordlists, and name entity lexicons provided to groups within dictionary had non-uniform pronunciation weights. the DARPA TransTAC project. When a word appeared in In previous works [1]-[3], MSA speech recognition systems these lists with more than one pronunciation, only the most that explicitly modeled diacritics in the acoustic model and frequent was selected. Pronunciations for the remaining 45k considered multiple pronunciations during decoding were words were generated using a statistical method trained using shown to outperform grapheme-based systems. In these the dictionaries listed above. We used an extension to the works, the Buckwalter Morphological Analyzer [4] was used CART-based method described in [5], in which we first to generate all possible pronunciations of a word, and acoustic predict probability densities of possible phones and then models were then trained using either manually vowelized perform Viterbi decoding applying a phoneme-based trigram transcripts [1], or by automatically generating labels when model. This approach is described in [6]. The single best performing force alignment of the training data. Both hypothesis was used as the resulting pronunciation. approaches obtained higher recognition accuracy than The multi-pronunciation dictionary was built in a similar comparable grapheme-based systems. When using multi- manner, except all pronunciations from manually compiled pronunciation dictionaries in recognition, further improvement dictionaries were included. The resulting dictionary contained was gained by assigning non-uniform weights to 1.7 pronunciations per word on average. pronunciations, for example, based on word counts obtained during forced alignment. B. Estimating Pronunciations Probabilities In this work we focus on one specific dialect of Arabic, We built two speech recognition systems that used the Iraqi-Arabic. For this dialect we cannot rely on off-the-shelf multi-pronunciation dictionary described above. The first, morphological analyzers, as Iraqi is typically not written, assigned uniform probabilities to pronunciation variants; the furthermore, in Iraqi pronunciation variants cannot simply be second assigned weights estimated using 450 hours of generated via grammatical rules. Therefore, we leverage acoustic model (AM) training data. Pronunciation weights pronunciation dictionaries, including the LDC Iraqi Arabic were estimated using the following algorithm: Morphological Lexicon to obtain pronunciations of Iraqi 1. Perform forced alignment, using a multi-pronunciation words. For words that did not appear in the manually dictionary, and generate labels. compiled lexicon we automatically generate a pronunciation 2. Use the labels generated to train an AM. using the method described in Section II-A. 3. Perform forced alignment using the AM trained above. We compared two variants of a multi-pronunciation system For a given word W with n pronunciations to a baseline system which uses a single pronunciation per ≤ ≤ pri 1 i n the pronunciation probability word. The first multi-pronunciation system has uniform is: weights assigned to all pronunciations. The second, uses weights computed via forced alignment. We show that both #()FA pr multi-pronunciation systems outperform the baseline and that i #C ( w )≠ 0 = using non-uniform pronunciation weights further improves Pi #()C w (1) recognition accuracy. Further gains were obtained by 1/n # C ( w )= 0 retraining the acoustic model using phonetic labels generated #()FA pr : The count that pr appears in the by a multi-pronunciation system. i i The remainder of the paper is organized as follows: In forced alignment. Section II we describe the multi-pronunciation dictionary, and n = #C ( w )∑ # FA ( pri ) (2) introduce a method to estimate pronunciation weights. In i=1 Section III we describe the baseline system, and compare the performance of the uniform and weighted multi-pronunciation systems. Finally, conclusion and future works are described in Section IV. 526 ≠ TABLE II If for some pronunciation #()FA pri =0 but #C ( w ) 0 then this COMPARISON OF ASR PERFORMANCE OF DIFFERENT SYSTEMS
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