Improving Recognition Accuracy of Urdu Weather Service by Identifying Out-of-Vocabulary Words
Abstract
Mobile based dialogue systems in local languages provide a very suitable information delivery channel. Many of the tasks can be addressed by designing and developing small vocabulary systems. However, as small vocabulary systems generally try to match each input word onto one of the words in the vocabulary, if inadvertently out of vocabulary (OOV) words are spoken, they are also mapped onto the closed set of words in vocabulary and reduce the accuracy. The current work addresses this issue. We present the development of mobile based dialogue system in local language (Urdu) to provide weather information to urban and rural populations. Performance of this speaker independent automatic speech recognition system (ASR) is evaluated by offline and online testing. In offline testing, based on unseen dataset limited to the speakers used for training the system, 100% accuracy is achieved. In online testing, 74.79% accuracy is achieved. Analysis shows that a significant reduction in accuracy is caused by out-of-vocabulary words (OOV) spoken by users. Phone-based model is then added to detect and reject OOV words and system accuracy improves to 88.24%.References
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