A new auditory-based speech processing system based on the biologically rooted property of the average localized synchrony detection (ALSD) is proposed. The system detects periodicity in the speech signal at Bark-scaled frequencies while reducing the response's spurious peaks and sensitivity to implementation mismatches, and hence presents a consistent and robust representation of the formants. The system is evaluated for its formant extraction ability while reducing spurious peaks. It is compared with other auditory-based and traditional systems in the tasks of vowel and consonant recognition on clean speech from the TIMIT database and in the presence of noise. The results illustrate the advantage of the ALSD system in extracting the formants and reducing the spurious peaks. They also indicate the superiority of the synchrony measures over the mean-rate in the presence of noise.

In this work, a feature-based system for the automatic classification of stop consonants, in speaker independent continuous speech, is reported. The system uses a new auditory based speech processing front-end that is based on the biologically rooted property of average localized synchrony detection (ALSD). It incorporates new algorithms for the extraction and manipulation of the acoustic-phonetic features that proved, statistically, to be rich in their information content. The experiments are performed on stop consonants extracted from the TIMIT database with additive white Gaussian noise at various signal-to-noise ratios. The obtained classification accuracy compares favorably with previous work. The results also showed a consistent improvement of 3% in the place detection over the generalized synchrony detector (GSD) system under identical circumstances on clean and noisy speech. This illustrates the superior ability of the ALSD to suppress the spurious peaks and produce a consistent and robust formant (peak) representation.

In this paper, the acoustic-phonetic characteristics of the American English stop consonants are investigated. Features studied in the literature are evaluated for their information content and new features are proposed. A statistically guided, knowledge-based, acoustic-phonetic system for the automatic classification of stops, in speaker independent continuous speech, is proposed. The system uses a new auditory-based front-end processing and incorporates new algorithms for the extraction and manipulation of the acoustic-phonetic features that proved to be rich in their information content. Recognition experiments are performed using hard decision algorithms on stops extracted from the TIMIT database continuous speech of 60 speakers (not used in the design process) from seven different dialects of American English. An accuracy of 96% is obtained for voicing detection, 90% for place of articulation detection and 86% for the overall classification of stops.

In this paper, a new auditory-based speech processing system based on the biologically rooted property of average localized synchrony detection (ALSD) is proposed. The system detects periodicity in the speech signal at Bark-scaled frequencies while reducing the response's spurious peaks and sensitivity to implementation mismatches, and hence presents a consistent and robust representation of the formants. The system is evaluated for its formant extraction ability while reducing spurious peaks. It is compared with other auditory-based front-end processing systems in the task of vowel recognition on clean speech from the TIMIT database and in the presence of noise. The results illustrate the advantage of the ALSD system in extracting the formants and reducing the spurious peaks. They also indicate the superiority of the synchrony measures over the mean-rate in the presence of noise.

The acoustic-phonetic characteristics and the automatic recognition of the American English fricatives are investigated. The acoustic features that exist in the literature are evaluated and new features are proposed. To test the value of the extracted features, a knowledge-based acoustic-phonetic system for the automatic recognition of fricatives, in speaker independent continuous speech, is proposed. The system uses an auditory-based front-end processing and incorporates new algorithms for the extraction and manipulation of the acoustic-phonetic features that proved to be rich in their information content. Several features, which describe the relative amplitude, location of the most dominant peak, spectral shape and duration of unvoiced portion, are combined in the recognition process. A recognition accuracy of 95% for voicing detection and 93% for place of articulation detection are obtained for TIMIT database continuous speech of 22 speakers from 5 different dialect regions.