Comparative Analysis of HTK and Sphinx in Vietnamese Speech Recognition

Dinhz, Hoa Minh; Nguyen, Tan Duy; Pham, Tho Duc

doi:10.17148/ijarcce.2015.45156

Cited by 1 publication

(3 citation statements)

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“…Figure 2 shows the results that were achieved decoding the test data from AN4 corpus. These were achieved on a PC running Windows and Cygwin, with a 2.6GHz Pentium 4 processor with 2 GB System RAM [12]. HTK decoder did not make any deletions, which gave it a slight advantage on the overall word error rate.…”

Section: Survey Of Tools For Asrmentioning

confidence: 99%

“…HTK decoder did not make any deletions, which gave it a slight advantage on the overall word error rate. Also, while HVite uses less memory during decoding, the time difference in running the test set is significant at 30 seconds [12], [13]. In addition to this HTK supports HCopy tools which provide a wealth of input/output combinations of model data and front-end features couple of which are not present in SPHINX but certain compatibility is provided.…”

Section: Survey Of Tools For Asrmentioning

confidence: 99%

“…Both of these are based on Hidden Markov Model(HMM) and are open source. Both frameworks can be used to develop, train, and test a speech model from existing corpus speech utterance data by using Hidden Markov modeling techniques [12]. Figure 2 shows the results that were achieved decoding the test data from AN4 corpus.…”

Section: Survey Of Tools For Asrmentioning

confidence: 99%

See 2 more Smart Citations

A survey of voice translation methodologies — Acoustic dialect decoder

Krupakar¹,

Rajvel²,

Bharathi³

et al. 2016

2016 International Conference on Information Communication and Embedded Systems (ICICES)

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Speech Translation has always been about giving source text/audio input and waiting for system to give translated output in desired form. In this paper, we present the Acoustic Dialect Decoder (ADD) -a voice to voice ear-piece translation device. We introduce and survey the recent advances made in the field of Speech Engineering, to employ in the ADD, particularly focusing on the three major processing steps of Recognition, Translation and Synthesis. We tackle the problem of machine understanding of natural language by designing a recognition unit for source audio to text, a translation unit for source language text to target language text, and a synthesis unit for target language text to target language speech. Speech from the surroundings will be recorded by the recognition unit present on the ear-piece and translation will start as soon as one sentence is successfully read. This way, we hope to give translated output as and when input is being read. The recognition unit will use Hidden Markov Models (HMMs) Based Tool-Kit (HTK), hybrid RNN systems with gated memory cells, and the synthesis unit, HMM based speech synthesis system HTS. This system will initially be built as an English to Tamil translation device.

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Section: Survey Of Tools For Asrmentioning

confidence: 99%

Section: Survey Of Tools For Asrmentioning

confidence: 99%