[vc_empty_space][vc_empty_space]
The Indonesian Language speech synthesizer based on the Hidden Markov Model
Jangtjik K.A.a, Lestari D.F.a
a Informatics/Computer Sciences Study Program, 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]© 2014 IEEE.Speech synthesizer is a technology which gives the computer a capability to speech text sequences. In this research, we develop a speech synthesizer for Indonesian Language based on the Hidden Markov Model (HMM). The Speech synthesizer using the HMM can produce more appropriate result than based on the syllable concatenation. There are some studies of speech synthesizers using HMM for Indonesian Language. However, it still has some problems such as it still cannot distinguish between vowel ‘e’ (‘e’ in ‘get’ is different from ‘e’ in ‘apple’); It cannot handle abbreviation, numbers, special characters, and foreign (English) terms widely. In this research, we also proposed some methods to solve those problems. To solve ‘e’ problem, this research divided the HMM for the 2 ‘e’ vowel. To solve the other problems, the ‘e’ rules, the abbreviation rules, the number rules, the special character rules, and the foreign term rules are made. To evaluate the synthesizer, we employ two methods: the Mean Opinion Score (MOS) to measure the naturalness of synthesized speech; and the Semantically Unpredictable Sentence (SUS) to measure the accuracy of the synthesized speech. Result shows that the developed speech synthesizer improved the naturalness of synthesized speech. It achieves 4.1 for MOS point and 96,07 % word accuracy.[/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]Indonesia,Indonesian languages,Mean opinion scores,MOS,Special characters,Speech synthesizer,SUS,Synthesized speech[/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]bahasa Indonesia,hidden markov model,MOS,speech synthesizer,SUS[/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/ICEECS.2014.7045211[/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]