Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed

Wavelet-based techniques for speech recognition

In this thesis, new wavelet-based techniques have been developed for the extraction of features from speech signals for the purpose of automatic speech recognition (ASR). One of the advantages of the wavelet transform over the short time Fourier transform (STFT) is its capability to process non-stationary signals. Since speech signals are not strictly stationary the wavelet transform is a better choice for time-frequency transformation of these signals. In addition it has compactly supported basis functions, thereby reducing the amount of computation as opposed to STFT where an overlapping window is needed. [Continues.

Detailed versus gross spectro-temporal cues for the perception of stop consonants

x+182hlm.;24c

Robust Classification of Stop Consonants Using Auditory-Based Speech Processing

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

Acoustic analysis of Sindhi speech - a pre-curser for an ASR system

The functional and formative properties of speech sounds are usually referred to as acoustic-phonetics in linguistics. This research aims to demonstrate acoustic-phonetic features of the elemental sounds of Sindhi, which is a branch of the Indo-European family of languages mainly spoken in the Sindh province of Pakistan and in some parts of India. In addition to the available articulatory-phonetic knowledge; acoustic-phonetic knowledge has been classified for the identification and classification of Sindhi language sounds. Determining the acoustic features of the language sounds helps to bring together the sounds with similar acoustic characteristics under the name of one natural class of meaningful phonemes. The obtained acoustic features and corresponding statistical results for a particular natural class of phonemes provides a clear understanding of the meaningful phonemes of Sindhi and it also helps to eliminate redundant sounds present in the inventory. At present Sindhi includes nine redundant, three interchanging, three substituting, and three confused pairs of consonant sounds. Some of the unique acoustic-phonetic features of Sindhi highlighted in this study are determining the acoustic features of the large number of the contrastive voiced implosives of Sindhi and the acoustic impact of the language flexibility in terms of the insertion and digestion of the short vowels in the utterance. In addition to this the issue of the presence of the affricate class of sounds and the diphthongs in Sindhi is addressed. The compilation of the meaningful language phoneme set by learning their acoustic-phonetic features serves one of the major goals of this study; because twelve such sounds of Sindhi are studied that are not yet part of the language alphabet. The main acoustic features learned for the phonological structures of Sindhi are the fundamental frequency, formants, and the duration — along with the analysis of the obtained acoustic waveforms, the formant tracks and the computer generated spectrograms. The impetus for doing such research comes from the fact that detailed knowledge of the sound characteristics of the language-elements has a broad variety of applications — from developing accurate synthetic speech production systems to modeling robust speaker-independent speech recognizers. The major research achievements and contributions this study provides in the field include the compilation and classification of the elemental sounds of Sindhi. Comprehensive measurement of the acoustic features of the language sounds; suitable to be incorporated into the design of a Sindhi ASR system. Understanding of the dialect specific acoustic variation of the elemental sounds of Sindhi. A speech database comprising the voice samples of the native Sindhi speakers. Identification of the language‘s redundant, substituting and interchanging pairs of sounds. Identification of the language‘s sounds that can potentially lead to the segmentation and recognition errors for a Sindhi ASR system design. The research achievements of this study create the fundamental building blocks for future work to design a state-of-the-art prototype, which is: gender and environment independent, continuous and conversational ASR system for Sindhi

Acoustic-Phonetic Features for the Automatic Classification of Stop Consonants

In this paper, the acoustic–phonetic characteristics of 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 articulation detection and 86% for the overall classification of stops

Hypernasal Speech Detection by Acoustic Analysis of Unvoiced Plosive Consonants

Las personas con un mecanismo velofaringeo defectuoso hablan con una resonancia nasal anormal (habla hipernasal). Métodos de análisis de voz para detección de hipernasaliad comúnmente usan las vocales y las vocales nasales. Sin embargo para obtener una evaluación más general de esta anormalidad es necesario analizar las paradas y las fricativas. Este estudio describe un método con alta capacidad de generalización para detección de hipernasalidad análisis de las consonantes oclusivas sordas españolas. Se muestra la importancia del análisis fonema por fonema, en contraste con la parametrización de la palabra completa que incluye segmentos irrelevantes desde el punto de vista de la clasificación. Los parámetros que correlacionan la incompetencia velofaringea (VPI) sobre las consonantes oclusivas sordas se usa en la fase de estimación de características. La clasificación se llevó a cabo usando una Maquina de Vector de Soporte (SVM), incluyendo el modelo de complejidad Rademacher con el objetivo de aumentar la capacidad de generalización. Rendimientos del 95.2% y del 92.7% fueron obtenidos en las etapas de elaboración y verificación para una repetida evaluación y clasificación de validación cruzada.People with a defective velopharyngeal mechanism speak with abnormal nasal resonance (hypernasal speech). Voice analysis methods for hypernasality detection commonly use vowels and nasalized vowels. However to obtain a more general assessment of this abnormality it is necessary to analyze stops and fricatives. This study describes a method with high generalization capability for hypernasality detection analyzing unvoiced Spanish stop consonants. The importance of phoneme-by-phoneme analysis is shown, in contrast with whole word parametrization which includes irrelevant segments from the classification point of view. Parameters that correlate the imprints of Velopharyngeal Incompetence (VPI) over voiceless stop consonants were used in the feature estimation stage. Classification was carried out using a Support Vector Machine (SVM), including the Rademacher complexity model with the aim of increasing the generalization capability. Performances of 95.2% and 92.7% were obtained in the processing and verification stages for a repeated cross-validation classifier evaluation