Çetin, A. EnisYardımcı, Y.Erzin, Engin2016-02-082016-02-081995-050736-7791http://hdl.handle.net/11693/27764Date of Conference: 9-12 May 1995Conference name: International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1995In this paper, a new set of speech feature representations for robust speech recognition in the presence of car noise are proposed. These parameters are based on subband analysis of the speech signal. Line Spectral Frequency (LSF) representation of the Linear Prediction (LP) analysis in subbands and cepstral coefficients derived from subband analysis (SUBCEP) are introduced, and the performances of the new feature representations are compared to mel scale cepstral coefficients (MELCEP) in the presence of car noise. Subband analysis based parameters are observed to be more robust than the commonly employed MELCEP representations.EnglishAcoustic noiseComputer simulationFeature extractionMarkov processesMathematical modelsNumerical analysisPolynomialsSpeech analysisVector quantizationParameter estimationPattern recognition systemsPerformanceCepstral coefficientLine spectral frequencyLinear prediction analysisMel scale cepstral coefficientSpeech signalSubband analysisCar noiseHidden Markov modelLinear predictive codingMel scale cepstral coefficientsSpeech recognition systemSpeech recognitionConference Paper10.1109/ICASSP.1995.479610