Abstract
AbstractIncorrect recognition of adjacent small words is considered one of the obstacles in improving the performance of automatic continuous speech recognition systems. The pronunciation variation in the phonemes of adjacent words introduces ambiguity to the triphone of the acoustic model and adds more confusion to the speech recognition decoder. However, small words are more likely to be affected by this ambiguity than longer words. In this paper, we present a data-driven approach to model the small words problem. The proposed method identifies the adjacent small words in the corpus transcription to generate the compound words. The unique compound words are then added to the expanded pronunciation dictionary, as well as to the language model as a new sentence. Results show a significant improvement of 2.16% in the word error rate compared to that of the Baseline speech corpus of Modern Standard Arabic broadcast news.KeywordsSpeech recognitionpronunciation variationsmall-wordphonetic dictionarylanguage modelModern Standard Arabic
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