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— Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint(More)
A recent trial of natural language call steering on live UK calls to the operator is described along with its results. The characteristics of the problem are described along with the acoustic, language, semantic and dialogue modelling approaches employed. Natural language call steering is found to be viable, with recognition and semantic accuracy the(More)
This paper focuses on comparison of two continuous space language modeling techniques, namely Tied–Mixture Language modeling (TMLM) and Neural Network Based Language Modeling (NNLM). Additionally, we report on using alternative feature representations for words and histories used in TMLM. Besides bigram co–occurrence based features we consider using NNLM(More)
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(More)