Performance evaluation of artificial neural networks for isolated Hindi digit recognition with LPC and MFCC

Aggarwal, Arpit; Sahay, Tanvi; Chandra, Mahesh

doi:10.1109/icaccs.2015.7324099

Cited by 8 publications

(1 citation statement)

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“…Speech recognition systems presently are capable of understanding speech input for vocabularies of thousands of words in operational environments [2]. Several research papers worked on performance evaluation using different feature extraction techniques like LPC, MFCC [11], [13] using different language corpus.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Error Pattern of HMM based Bangla ASR

Aura¹,

Rahimi²,

Baroi³

2020

IJIGSP

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Speech Recognition research has been ongoing for more than 80 years. Various attempts have been made to develop and improve speech recognition process around the world. Research on ASR by machine has attracted much attention over the last few decades. Bengali is largely spoken all over the world. There are lots of scopes yet to explore in the research regarding offline automatic Bangla speech recognition system. In our work, a moderate size speech corpus and a HMM based speech recognizer have been built to analyze the error pattern. Audio recordings have been collected from different persons in both quiet and noisy area. Live test has been carried out also to check the performance of the model individually. The percentage of the error and the percentage of correction with the created models are presented in this paper along with the results obtained during the live test. Finally, the results are analyzed to get the error pattern needed for future development.

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Section: Introductionmentioning

confidence: 99%