A design and implementation of HMM based Mongolian speech recognition system

Ayush, Altangerel; Bayanduuren, Damdinsuren

doi:10.1109/ifost.2013.6616910

Cited by 3 publications

(1 citation statement)

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“…Hidden Markov Models (HMM), Artificial Neural Network (ANN), Dynamic Time Warping (DTW), and Gaussian Mixture Models (GMM) are widely used machine learning techniques for speech pattern recognition. Ayush and Damdinsuren [29] presented HMM based isolated words of Mongolian language speech recognition system. Feature vectors are computed by using MFCC method.…”

Section: Acoustic Pattern Classificationmentioning

confidence: 99%

Automatic bimodal audiovisual speech recognition: A review

Kandagal

Udayashankara²

2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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Human computer interaction (HCI) is very crucial in our day-to-day activity. Speech is one of the essential and intuitive ways to interact with machines such as Smartphone, which has multiple sensors as microphone, camera, etc. An efficient performance speech recognition system improves interaction between man and machines by making latter more receptive to user needs. Such system has Automatic speech recognition (ASR) engine, which is facing a unique challenge of accuracy in recognition rate. By integrating acoustic signal feature vectors with the visual features, a more robust audiovisual speech recognition engine (AVSR) could be developed for real environmental scenarios. This paper presents past research and development in the field of ASR and AVSR technologies. It describes key technological perspective and admiration of the fundamental progress in ASR and AVSR. The objective of this review is to summarize and compare some of the well-known methods experimented by previous researchers, and to conclude with direction on future research proficiency in HCI system using ASR and AVSR engine.

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Section: Acoustic Pattern Classificationmentioning

confidence: 99%