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Related Experiment Video

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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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DWT features performance analysis for automatic speech recognition of Urdu.

Hazrat Ali1, Nasir Ahmad2, Xianwei Zhou3

  • 1Machine Learning Group, Department of Computing, City University London, Northampton Square, EC1V 0HB London, UK ; School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083 Beijing, China.

Springerplus
|February 13, 2015
PubMed
Summary
This summary is machine-generated.

This study compares Discrete Wavelets Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC) for Urdu Automatic Speech Recognition. DWT features showed better performance in recognizing frequently used Urdu words.

Keywords:
Automatic speech recognitionDiscrete wavelet transformsLinear discriminant analysisMel-frequency cepstral coefficientsUrdu isolated words recognition

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Area of Science:

  • Speech Processing
  • Signal Analysis
  • Machine Learning

Background:

  • Automatic Speech Recognition (ASR) for Urdu is challenging due to linguistic diversity.
  • Feature extraction is crucial for improving ASR accuracy.
  • Existing research often focuses on MFCC, necessitating exploration of alternative features like DWT.

Purpose of the Study:

  • To comparatively analyze the effectiveness of Discrete Wavelets Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC) for Urdu ASR.
  • To evaluate feature extraction methods for isolated Urdu word recognition.
  • To determine optimal features for Urdu speech recognition systems.

Main Methods:

  • Extracted DWT and MFCC features from 100 isolated Urdu words, spoken by 10 different speakers.
  • Utilized a balanced corpus covering various ages and dialects.
  • Employed Linear Discriminant Analysis for classification.
  • Compared confusion matrices from DWT and MFCC feature sets.

Main Results:

  • Experimental results indicate differences in classification accuracy between DWT and MFCC features.
  • The study provides a comparative analysis of feature performance for Urdu ASR.
  • DWT-based features demonstrated potential for improved Urdu speech recognition.

Conclusions:

  • Feature selection significantly impacts Urdu ASR performance.
  • DWT presents a viable alternative to MFCC for Urdu speech recognition.
  • Further research can optimize feature extraction and classification for Urdu ASR.