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On the use of morphological analysis for dialectal Arabic speech recognition
- Proc. ICSLP’06
"... Arabic has a large number of affixes that can modify a stem to form words. In automatic speech recognition (ASR) this leads to a high out-of-vocabulary (OOV) rate for typical lexicon size, and hence a potential increase in WER. This is even more pronounced for dialects of Arabic where additional aff ..."
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Arabic has a large number of affixes that can modify a stem to form words. In automatic speech recognition (ASR) this leads to a high out-of-vocabulary (OOV) rate for typical lexicon size, and hence a potential increase in WER. This is even more pronounced for dialects of Arabic where additional affixes are often introduced and the available data is typically sparse. To address this problem we introduce a simple word decomposition algorithm which only requires a text corpus and a predefined list of affixes. Using this al-gorithm to create the lexicon for Iraqi Arabic ASR results in about 10 % relative improvement in word error rate (WER). Also using the union of the segmented and unsegmented vocabularies and in-terpolating the corresponding language models results in further WER reduction. The net WER improvement is about 13%. 1.
Hybrid Language Models Using Mixed Types of Sub-lexical Units for Open Vocabulary German LVCSR
"... German is a highly inflected language with a large number of words derived from the same root. It makes use of a high degree of word compounding leading to high Out-of-vocabulary (OOV) rates, and Language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary C ..."
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German is a highly inflected language with a large number of words derived from the same root. It makes use of a high degree of word compounding leading to high Out-of-vocabulary (OOV) rates, and Language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary Continuous Speech Recognition (LVCSR) becomes a natural choice. In this paper, we investigate the use of mixed types of sub-lexical units in the same recognition lexicon. Namely, morphemic or syllabic units combined with pronunciations called graphones, normal graphemic morphemes or syllables along with full-words. This mixture of units is used for building hybrid LMs suitable for open vocabulary LVCSR where the system operates over an open, constantly changing vocabulary like in broadcast news, political debates, etc. A relative reduction of around 5.0 % in Word Error Rate (WER) is obtained compared to a traditional full-words system. Moreover, around 40 % of the OOVs are recognized. Index Terms: open vocabulary, morpheme, syllable, graphone 1.
Sub-Lexical Language Models for German LVCSR
- in IEEE workshop on Spoken Language Translation
, 2010
"... One of the major difficulties related to German LVCSR is the rich morphology nature of German, leading to high outof-vocabulary (OOV) rates, and high language model (LM) perplexities. Normally, compound words make up an essential fraction of the German vocabulary. Most compound OOVs are composed of ..."
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One of the major difficulties related to German LVCSR is the rich morphology nature of German, leading to high outof-vocabulary (OOV) rates, and high language model (LM) perplexities. Normally, compound words make up an essential fraction of the German vocabulary. Most compound OOVs are composed of frequent in-vocabulary words. Here, we investigate the use of sub-lexical LMs based on different approaches for word decomposition, namely supervised and unsupervised decomposition, as well as decomposition derived from grapheme-to-phoneme (G2P) conversion. In the later approach, we augment a normal word model with a set of grapheme-phoneme pairs called graphones used to model the OOV words. A novel approach is proposed to select the representative graphone sequences for OOVs based on unsupervised decomposition and word-pronunciation alignment. We obtain relative reductions in word error rate (WER) from 4.2 % to 6.5 % with respect to a comparable full-words system. Index Terms — Speech recognition, language model, sub-lexical, graphone, German
Analysis of Morph-Based Speech Recognition and the Modeling of Out-of-Vocabulary Words Across Languages
"... We analyze subword-based language models (LMs) in large-vocabulary continuous speech recognition across four “morphologically rich ” languages: ..."
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We analyze subword-based language models (LMs) in large-vocabulary continuous speech recognition across four “morphologically rich ” languages:
Multi-words in the Czech TV/radio news transcription system
- Proceedings of the 11th International Conference Speech and Computer, SPECOM’2006, Sankt Peterburg
, 2006
"... This article explores influence of multi-words (compound words) in the continuous speech recognition system of our Czech TV/radio News Transcription system. The main aim is to support recognition of short words, which are often misrec-ognized. Short words are joined with frequent longer words into a ..."
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This article explores influence of multi-words (compound words) in the continuous speech recognition system of our Czech TV/radio News Transcription system. The main aim is to support recognition of short words, which are often misrec-ognized. Short words are joined with frequent longer words into a multi-word. Two measures for multi-words selection are tested. The first measure is based on pointwise mutual infor-mation, the second is based on occurrence frequency. Occur-rence frequency based measure outperformed pointwise mutual information based measure. Adding multi-words increased per-formance of continuous speech recognition system and reduced misrecognition of short words. 1.
Vocabulary German LVCSR
, 2016
"... German is a highly inflected language with a large number of words derived from the same root. It makes use of a high de-gree of word compounding leading to high Out-of-vocabulary (OOV) rates, and Language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary ..."
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German is a highly inflected language with a large number of words derived from the same root. It makes use of a high de-gree of word compounding leading to high Out-of-vocabulary (OOV) rates, and Language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary Continuous Speech Recognition (LVCSR) becomes a natural choice. In this paper, we investigate the use of mixed types of sub-lexical units in the same recognition lexicon. Namely, mor-phemic or syllabic units combined with pronunciations called graphones, normal graphemic morphemes or syllables along with full-words. This mixture of units is used for building hy-brid LMs suitable for open vocabulary LVCSR where the sys-tem operates over an open, constantly changing vocabulary like in broadcast news, political debates, etc. A relative reduction of around 5.0 % in Word Error Rate (WER) is obtained compared to a traditional full-words system. Moreover, around 40 % of the OOVs are recognized. Index Terms: open vocabulary, morpheme, syllable, graphone 1.
Compound Word Recombination for German LVCSR
"... Compound words are a difficulty for German speech recognition systems since they cause high out-of-vocabulary and word error rates. State of the art approaches augment the language model by the fragments of compounds in order to increase lexical coverage, lower the perplexity and out-of-vocabulary r ..."
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Compound words are a difficulty for German speech recognition systems since they cause high out-of-vocabulary and word error rates. State of the art approaches augment the language model by the fragments of compounds in order to increase lexical coverage, lower the perplexity and out-of-vocabulary rate. The fragments are tagged in order to concatenate subsequent equally tagged fragments in the recognition result, but this does not guarantee the recombination of proper words. Such recombination techniques neglect the large vocabulary of the language model training data for recombination although most compounds are covered by it. In this paper, we investigate the use of this vocabulary for the recombination of compound words from the recognition result. The approach is tested on two large vocabulary tasks on top of full-word and fragment based language models and achieves good improvements of 3– 7 % relative over the baseline compound-sensitive word error rate.
INTERSPEECH 2011 Morpheme Based Factored Language Models for German LVCSR
"... German is a highly inflectional language, where a large number of words can be generated from the same root. It makes a liberal use of compounding leading to high Out-of-vocabulary (OOV) rates, and poor Language Model (LM) probability estimates. Therefore, the use of morphemes for language modeling ..."
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German is a highly inflectional language, where a large number of words can be generated from the same root. It makes a liberal use of compounding leading to high Out-of-vocabulary (OOV) rates, and poor Language Model (LM) probability estimates. Therefore, the use of morphemes for language modeling is considered a better choice for Large Vocabulary Continuous Speech Recognition (LVCSR) than the full-words. Thereby, better lexical coverage and less LM perplexities are achieved. On the other side, the use of Factored Language Models (FLMs) is considered a successful approach that allows the integration of many information sources to get better LM probability estimates. In this paper, we try a combined methodology for language modeling where both morphological decomposition and factored language modeling are used in one model called morpheme based FLM. Finally, we obtain around 2.5 % relative reduction in Word Error Rate (WER) with respect to a traditional full-words system. Index Terms: morpheme, factored language model, German 1.
