The Hokkien isolated word recognition system based on FPGA

Li, Lin; Xu, Wenhao; Wu, Jiawen; He, Shan; Li, Xiaochao

doi:10.1109/icasid.2014.7064971

Cited by 4 publications

(10 citation statements)

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“…Moreover, during acquisition process the EDADC removes the low amplitude noise across the signal base line. This phenomenon is called the noise thresholding and it improves the post speech classification accuracy [7][8][9]. Furthermore, an additional signal enhancement is achieved with the de-noising process, realized with an adaptive rate filtering method [14].…”

Section: Resultsmentioning

confidence: 99%

“…This choice of FCDmin, removes any DC offset from the incoming speech segment. It improves the classification accuracy [6][7][8][9][10]26]. Furthermore, it filters out the first harmonics of the pitch information from the incoming speech segment and therefore makes the classification process independent from the speaker's gender.…”

Section: B the Windowing Resampling And Filteringmentioning

confidence: 99%

“…The spectral coefficients only present the static characteristics of the intended speech word. However, the human auditory system sensitivity is higher for the dynamic characteristics of sound [1][2][3][4][5][6][7][8][9][10]. Following this approach the intended word dynamic features are derived from its spectral coefficients.…”

Section: The Parameters Extractionmentioning

confidence: 99%

“…Later on, these extracted parameters are employed to classify the incoming speech. It allows realizing effective and robust machine based speech recognition systems [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The classical speech recognition systems are time invariant in nature [8][9][10]. It can lead towards a useless usage of the system resources and power consumption [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Isolated Speech Recognition Based on the Adaptive Rate Processing and Analysis

Qaisar¹

2018

Preprint

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This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.

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Section: Resultsmentioning

confidence: 99%

Section: B the Windowing Resampling And Filteringmentioning

confidence: 99%

Section: The Parameters Extractionmentioning

confidence: 99%

“…Later on, these extracted parameters are employed to classify the incoming speech. It allows realizing effective and robust machine based speech recognition systems [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The classical speech recognition systems are time invariant in nature [8][9][10]. It can lead towards a useless usage of the system resources and power consumption [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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