Speaker Recognition For Digital Forensic Audio Analysis Using Learning Vector Quantization Method

Manurung, Danny Bastian; Dirgantoro, Burhanuddin; Setianingsih, Casi

doi:10.1109/iotais.2018.8600852

Cited by 14 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this stage of pre-emphasis, that is the process of filtering sound signals to be clearer when heading to the next process [17]- [19]. In the pre-emphasis stage, filtering is performed on the sound file signal using a filter coefficient of 0.097.…”

Section: B Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Speech Recognition Implementation Using MFCC and DTW Algorithm for Home Automation

Haq¹,

Nasrun²,

Setianingsih³

et al. 2020

EECSI

Self Cite

View full text Add to dashboard Cite

The use of speech recognition as part of home automation, especially for smart homes, is an exciting thing that is still being developed. That is because of human needs for comfort, convenience, quality of life, and better safety. Speech recognition built in this study is used as a device to control smart home devices by identifying the commands spoken by users, especially in a state of clean speech. The command used is a predetermined consecutive word. For the extraction of voice commands, the MFCC algorithm is used to match spoken words with templates using the Dynamic Time Warping (DTW) algorithm. DTW algorithm can find the difference between 2time series that have different lengths of time. The results of the accuracy of this system by using these algorithms were successfully carried out by 86.67%, with an average time required to identify the commands of 5.28 seconds.

show abstract

Section: B Preprocessingmentioning

confidence: 99%

“…This process makes the sound signal into several sample N frames. The first frame consists of the first sample N [19]. The second frame consists of M samples after the first frame and overlaps with N-M samples.…”

Section: Framing and Applying Hamming Windowmentioning

confidence: 99%