A Survey on English Digit Speech Recognition using HMM
Abstract: Speech technology and systems in human computer interaction have witnessed a stable and remarkable advancement over the last two decades. Today, speech technologies are commercially available for an unlimited but an interesting range of tasks. These technologies enable machines to respond correctly and reliably to human voices, and give useful and valuable services. Speech recognition system recognizes the speech samples. Recognition phase of Speech Recognition Process using Hidden Markov Model. Preprocessing, Feature Extraction and Recognition three steps and Hidden Markov Model (used in recognition phase) are used to complete Automatic Speech Recognition System. Hidden Markov Model (HMM) provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique This paper focuses on all English digits from (Zero through Nine), which is based on isolated words structure.
Keywords: Speech Recognition, HMM, MFCC, LPC
How to Cite?: Vaibhavi Trivedi, "A Survey on English Digit Speech Recognition using HMM", Volume 2 Issue 3, March 2013, International Journal of Science and Research (IJSR), Pages: 247-253, https://www.ijsr.net/getabstract.php?paperid=IJSRON2013583, DOI: https://dx.doi.org/10.21275/IJSRON2013583