Design of Manipuri Keywords Spotting System using HMM

Rahul, Laishram; Nandakishor, Salam; Singh, L. Joyprakash; Dutta, Sushanta Kabir

doi:10.1109/ncvpripg.2013.6776249

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Rahul. L. et al (2013) [30] have discussed in their research paper about implementation of phoneme. They have used HMM tool kit (HTK), version3.4 the better implementation the system.…”

Section: Manipuri Languagementioning

confidence: 99%

A Review on Automatic Speech Recognition System in Indian Regional Languages

Siddharth¹,

Prashantkumar²,

Sunil³

2018

IJCA

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Speech Recognition is the system whose allows a user to use their voice in the form of input data. It may be used to command text to the computer and give order to the computer system. Speech technologies are commonly used available for an unlimited but most important range of tasks. Older speech recognition application needs to identify each single world by the different phases. This process allow to the machine to conclude where one word begins and the next word stops. This type of speech recognition application are still used to direct to the computer's system. And operate applications like web browser and spread sheets. New speech recognition system allow a user to order text fluently into the system. The system that allow continuous speech are generally designed to recognize text and format it, rather than controlling the computer system itself.in This research paper studied various types of techniques which is mostly used in automatic speech recognition.

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“…Rahul. L. et al (2013) [30] have discussed in their research paper about implementation of phoneme. They have used HMM tool kit (HTK), version3.4 the better implementation the system.…”

Section: Manipuri Languagementioning

confidence: 99%

A Review on Automatic Speech Recognition System in Indian Regional Languages

Siddharth¹,

Prashantkumar²,

Sunil³

2018

IJCA

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“…Thus, we used 29 different phonetic units and a silence, after merging the phonetic units for similar sounds. Then, we assigned to these 29 phonetic units their corresponding ASCII codes while the silence is denoted as "sil" [11], [12].…”

Section: Speech Corpusmentioning

confidence: 99%

An HMM based semi-automatic syllable labeling system for Manipuri language

Nandakishor

Dutta

Singh

2015

International Conference on Computing, Communication &Amp; Automation

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This paper describes the development of a Semi-Automatic Syllable Labeling System for the Manipuri language which is a scheduled Indian language of Tibeto-Burman origin and very highly agglutinative language. A system is developed using HMM tool kit (HTK), version 3.4 and later analyzed using popular speech analysis software (WaveSurfer), version 8.5.8. The HTK has been used for training, segmentation and labeling of the data. The system can segment the manual phonetic transcription into syllables with timing information. The syllable offset and onset of the automatic and the manual syllable labeling are compared to calculate the "Time Deviation" (W) of each syllable. We have found the "Average Deviation" of the syllables of our system is 25 ms. The Detection Rate of the syllables of the system is calculated by considering various "Time Deviations".

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“…This includes developing a phonetic engine for language identification task using a very small dataset of 5 hours [14]. Further, on this data, phoneme-based KWS has also been demonstrated stating an accuracy of 65.24% [15]. In [16], the effect of phonetic modeling on Manipuri digit recognition had also been studied.…”

Section: Introductionmentioning

confidence: 99%

Development of Large Vocabulary Speech Recognition System with Keyword Search for Manipuri

Patel¹,

Dn²,

Fathima³

et al. 2018

Interspeech 2018

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Research in Automatic Speech Recognition (ASR) has witnessed a steep improvement in the past decade (especially for English language) where the variety and amount of training data available is huge. In this work, we develop an ASR and Keyword Search (KWS) system for Manipuri, a low-resource Indian Language. Manipuri (also known as Meitei), is a Tibeto-Burman language spoken predominantly in Manipur (a northeastern state of India). We collect and transcribe telephonic read speech data of 90+ hours from 300+ speakers for the ASR task. Both state-of-the-art Gaussian Mixture-Hidden Markov Model (GMM-HMM) and Deep Neural Network-Hidden Markov Model (DNN-HMM) based architectures are developed as a baseline. Using the collected data, we achieve better performance using DNN-HMM systems, i.e., 13.57% WER for ASR and 7.64% EER for KWS. The KALDI speech recognition tool-kit is used for developing the systems. The Manipuri ASR system along with KWS is integrated as a visual interface for demonstration purpose. Future systems will be improved with more amount of training data and advanced forms of acoustic models and language models.

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Design of Manipuri Keywords Spotting System using HMM

Cited by 7 publications

References 4 publications

A Review on Automatic Speech Recognition System in Indian Regional Languages

A Review on Automatic Speech Recognition System in Indian Regional Languages

An HMM based semi-automatic syllable labeling system for Manipuri language

Development of Large Vocabulary Speech Recognition System with Keyword Search for Manipuri

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