title: Formant analysis in dysphonic patients and automatic Arabic digit speech recognition.
abstract: OBJECTIVE
  There has been a growing interest in objective assessment of speech in dysphonic patients for the classification of the type and severity of voice pathologies using automatic speech recognition (ASR). The aim of this work was to study the accuracy of the conventional ASR system (with Mel frequency cepstral coefficients (MFCCs) based front end and hidden Markov model (HMM) based back end) in recognizing the speech characteristics of people with pathological voice.
  
  
  METHODS
  The speech samples of 62 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits were taken as an input. The distribution of the first four formants of the vowel /a/ was extracted to examine deviation of the formants from normal.
  
  
  RESULTS
  There was 100% recognition accuracy obtained for Arabic digits spoken by normal speakers. However, there was a significant loss of accuracy in the classifications while spoken by voice disordered subjects. Moreover, no significant improvement in ASR performance was achieved after assessing a subset of the individuals with disordered voices who underwent treatment.
  
  
  CONCLUSIONS
  The results of this study revealed that the current ASR technique is not a reliable tool in recognizing the speech of dysphonic patients.
authors.affiliation: Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. ghulam@ksu.edu.sa
authors.forename: Ghulam
authors.initials: G
authors.lastname: Muhammad
authors.forename: Tamer A
authors.initials: TA
authors.lastname: Mesallam
authors.forename: Khalid H
authors.initials: KH
authors.lastname: Malki
authors.forename: Mohamed
authors.initials: M
authors.lastname: Farahat
authors.forename: Mansour
authors.initials: M
authors.lastname: Alsulaiman
authors.forename: Manal
authors.initials: M
authors.lastname: Bukhari
mesh_terms: D000293:Adolescent; D000328:Adult; D001331:Automation; D055154:Dysphonia / Q000175:diagnosis* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007802:Language; D008297:Male; D008875:Middle Aged; D010700:Phonetics; D013060:Speech; D049250:Speech Recognition Software; D055815:Young Adult