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Spoken query for Qur’anic verse information retrieval
Ridwan T.a, Lestari D.P.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 IEEE.Most of information retrieval (IR) systems for Qur’an use text as their input query, whether they use the Alphabetic script or the Arabic script to represent the query. Thus, required IR user to know how to write the query. For searching the Qur’an verses, it is possible that IR user knows how to pronounce the query, but does not have enough knowledge about how to write Arabic letters to represent the query when search for a Qur’an verse. In this case, speech can be an alternative as the input to the IR system. In this work, we develop a spoken query IR based on the Hidden Markov Model acoustic models and the n- gram language model for its automatic speech recognition system. Both models are trained by using all verses of the Qur’an. The Inference Network Model and the well-known Vector Space Model are employed for its IR system. For the speech recognition system, average of word error rate are 7.41% for closed speakers, and 18.53% for open speakers. For the IR system, the best query formulation for the Inference Network is achieved by using input queries consisting of phrase of 2 words with the average value of Mean Reciprocal Rank is 0,922475, while for the Vector Space Model is achieved by using input query consisting of one word with the average value of Mean Reciprocal Rank is 0,9308.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Automatic speech recognition system,Inference network,Mean reciprocal ranks,N-gram language models,Qur’an Verse,Speech recognition systems,Spoken Query,Vector space models[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Inference Network,Information Retrieval,Qur’an Verse,Speech Recognition,Spoken Query,Vector Space Model[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICSDA.2017.8384422[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]