A Review on Different Approaches for Speech Recognition System

Saksamudre, Suman K.; Shrishrimal, Pukhraj P.; Deshmukh, Ratnadeep R.

doi:10.5120/20284-2839

Cited by 42 publications

(18 citation statements)

References 9 publications

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“…HMM sangat berpotensi untuk dikembangkan sesuai dengan kasus pengenalan suara yang hendak dibangun [12]. Hal ini dikarenakan HMM memiliki framework yang dibagun dari unsur matematis, mudah diterapkan dan layak secara komputasi [20]. Berdasarkan Tabel 4, penerapan HMM pada bidang pendidikan serta bisnis dan industri, memiliki peluang yang besar untuk dikembangkan, sedangkan untuk penerapan HMM di bidang ekonomi memiliki tingkat keberhasilan yang sempurna.…”

Section: A Unsur Dan Arsitektur Hmmunclassified

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

Jollyta

Oktarina

Johan³

2020

JEPIN

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Teknologi pengenal suara (speech recognition) merupakan teknologi yang berkembang pesat dalam bidang kecerdasan buatan (artificial intelligent). Saat ini, teknologi pengenal suara menjadi hal yang komersil melalui berbagai media teknologi seperti smartphone dan komputer. Salah satu pembentuk struktur pengenal suara agar dapat bekerja pada perangkat tersebut adalah model statistik pengenal suara Hidden Markov Model (HMM). Penerapan HMM pada berbagai kasus menunjukkan bahwa model ini cocok dengan berbagai macam data. Tulisan ini merupakan sebuah tinjauan untuk model HMM yang bertujuan untuk memberikan gambaran dan pemahaman terhadap kinerja HMM melalui rangkuman sejumlah penelitian yang digunakan dalam berbagai data. Penerapan HMM tersebut menunjukkan optimalisasi kinerja HMM dan tinjauan terhadap sejumlah penelitian menunjukkan bahwa tingkat keberhasilan HMM dalam mengenali data mencapai 71.43%.

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Section: A Unsur Dan Arsitektur Hmmunclassified

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

Jollyta

Oktarina

Johan³

2020

JEPIN

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“…Neural Network Models are having capability to solve more complicated recognition tasks but as vocabulary size increases performance goes down compare to HMM [8]. Neural network allows ASR system to recognize complex speech such as spontaneous or connected as it is difficult to identify boundaries between two successive connected words.…”

Section: ) Nnmmentioning

confidence: 99%

Automatic Speech Recognition Systems for Regional Languages in India

2019

ijrte

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Speech recognition systems has made remarkable progress in last ¬few decades such as Siri, Google assistant, Cortana. For improving the automation in services of all sectors including medical, agriculture, voice dialling, directory services, education, automobile etc., ASR systems must be built for regional languages as most of the Indian population in not familiar with English. Lots of work is done for English language but not for regional languages in India. Developing ASR and ASU systems will change the scenario of current service sector. There are many challenges in building ASR system, Noise reduction is a one of the challenging and still unsolved parameters which affects a lot on performance of any ASR system. Basically, three models required for building any ASR systems- Language model, acoustic model and pronunciation model. In this paper, discussed various parameters affecting on building ASR systems, development of ASR systems, Tools and Techniques used for building an ASR system and research on regional languages ASR system. Deep Neural network (DNN) provides a better way of recognising a speech and accuracy is high.

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“…In this research, MFCC is chosen as the method for feature extraction as it is commonly used for problems involving sound, especially for speech recognition [16][17][18][19].…”

Section: Mel Frequency Cepstral Coefficient (Mfcc)mentioning

confidence: 99%

Kopyor Coconut Detection Using Sound-Based Dynamic Time Warping Method

Dhelika¹,

Nugroho²,

Bayu³

et al. 2019

Jurnal Ilmu Komputer dan Informasi

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Kopyor coconut is a coconut that has genetic abnormalities which cause the coconut meat to have a unique texture and is detached from the coconut shell. Its uniqueness attracts many enthusiasts resulting in a high economic value, 4-5 times that of the ordinary coconut. From its external appearance, kopyor coconut does not differ with ordinary coconut and this poses a challenge in the detection stage. To date, both farmers and sellers use a traditional approach by listening to the sound of whisk from kopyor coconut to detect them. Unfortunately, this approach relies heavily on experience and expertise of the person. Therefore, a new detection approach is proposed based on sound recognition using Mel Frequency Cepstrum Coefficient (MFCC) as the method for feature extraction and Dynamic Time Warping (DTW) as the method for feature matching. Objects that will be detected are kopyor coconuts and ordinary coconut which has grown mature. By implementing both methods, a program has been developed to detect kopyor coconut with an accuracy of 93.8%.

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A Review on Different Approaches for Speech Recognition System

Cited by 42 publications

References 9 publications

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

Automatic Speech Recognition Systems for Regional Languages in India

Kopyor Coconut Detection Using Sound-Based Dynamic Time Warping Method

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