Rhythmic unit extraction and modelling for automatic language identification

International audienceThis paper deals with an approach to Automatic Language Identification based on rhythmic modelling. Beside phonetics and phonotactics, rhythm is actually one of the most promising features to be considered for language identification, even if its extraction and modelling are not a straightforward issue. Actually, one of the main problems to address is what to model. In this paper, an algorithm of rhythm extraction is described: using a vowel detection algorithm, rhythmic units related to syllables are segmented. Several parameters are extracted (consonantal and vowel duration, cluster complexity) and modelled with a Gaussian Mixture. Experiments are performed on read speech for 7 languages (English, French, German, Italian, Japanese, Mandarin and Spanish) and results reach up to 86 ± 6% of correct discrimination between stress-timed mora-timed and syllable-timed classes of languages, and to 67 ± 8% percent of correct language identification on average for the 7 languages with utterances of 21 seconds. These results are commented and compared with those obtained with a standard acoustic Gaussian mixture modelling approach (88 ± 5% of correct identification for the 7-languages identification task)

Sperry Univac speech communications technology

Technology and systems for effective verbal communication with computers were developed. A continuous speech recognition system for verbal input, a word spotting system to locate key words in conversational speech, prosodic tools to aid speech analysis, and a prerecorded voice response system for speech output are described

Automatic Blind Syllable Segmentation for Continuous Speech

In this paper a simple practical method for blind segmentation of continuous speech into its constituent syllables is presented. This technique which uses amplitude onset velocity and coarse spectral makeup to identify syllable boundaries is tested on a corpus of continuous speech and compared with an established segmentation algorithm. The results show substantial performance benefit using the proposed algorithm

Automatic Blind Syllable Segmentation for Continuous Speech

In this paper a simple practical method for blind segmentation of continuous speech into its constituent syllables is presented. This technique which uses amplitude onset velocity and coarse spectral makeup to identify syllable boundaries is tested on a corpus of continuous speech and compared with an established segmentation algorithm. The results show substantial performance benefit using the proposed algorithm

On segments and syllables in the sound structure of language: Curve-based approaches to phonology and the auditory representation of speech.

http://msh.revues.org/document7813.htmlInternational audienceRecent approaches to the syllable reintroduce continuous and mathematical descriptions of sound objects designed as ''curves''. Psycholinguistic research on oral language perception usually refer to symbolic and highly hierarchized approaches to the syllable which strongly differenciate segments (phones) and syllables. Recent work on the auditory bases of speech perception evidence the ability of listeners to extract phonetic information when strong degradations of the speech signal have been produced in the spectro-temporal domain. Implications of these observations for the modelling of syllables in the fields of speech perception and phonology are discussed.Les approches récentes de la syllabe réintroduisent une description continue et descriptible mathématiquement des objets sonores: les courbes. Les recherches psycholinguistiques sur la perception du langage parlé ont plutôt recours à des descriptions symboliques et hautement hiérarchisées de la syllabe dans le cadre desquelles segments (phones) et syllabes sont strictement différenciés. Des travaux récents sur les fondements auditifs de la perception de la parole mettent en évidence la capacité qu'ont les locuteurs à extraire une information phonétique alors même que des dégradations majeures du signal sont effectuées dans le domaine spectro-temporel. Les implications de ces observations pour la conception de la syllabe dans le champ de la perception de la parole et en phonologie sont discutées

Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution

In this article, we present an automatic technique for recognizing emotional states from speech signals. The main focus of this paper is to present an efficient and reduced set of acoustic features that allows us to recognize the four basic human emotions (anger, sadness, joy, and neutral). The proposed features vector is composed by twenty-eight measurements corresponding to standard acoustic features such as formants, fundamental frequency (obtained by Praat software) as well as introducing new features based on the calculation of the energies in some specific frequency bands and their distributions (thanks to MATLAB codes). The extracted measurements are obtained from syllabic units’ consonant/vowel (CV) derived from Moroccan Arabic dialect emotional database (MADED) corpus. Thereafter, the data which has been collected is then trained by a k-nearest-neighbor (KNN) classifier to perform the automated recognition phase. The results reach 64.65% in the multi-class classification and 94.95% for classification between positive and negative emotions

An acoustic-phonetic approach in automatic Arabic speech recognition

In a large vocabulary speech recognition system the broad phonetic classification technique is used instead of detailed phonetic analysis to overcome the variability in the acoustic realisation of utterances. The broad phonetic description of a word is used as a means of lexical access, where the lexicon is structured into sets of words sharing the same broad phonetic labelling. This approach has been applied to a large vocabulary isolated word Arabic speech recognition system. Statistical studies have been carried out on 10,000 Arabic words (converted to phonemic form) involving different combinations of broad phonetic classes. Some particular features of the Arabic language have been exploited. The results show that vowels represent about 43% of the total number of phonemes. They also show that about 38% of the words can uniquely be represented at this level by using eight broad phonetic classes. When introducing detailed vowel identification the percentage of uniquely specified words rises to 83%. These results suggest that a fully detailed phonetic analysis of the speech signal is perhaps unnecessary. In the adopted word recognition model, the consonants are classified into four broad phonetic classes, while the vowels are described by their phonemic form. A set of 100 words uttered by several speakers has been used to test the performance of the implemented approach. In the implemented recognition model, three procedures have been developed, namely voiced-unvoiced-silence segmentation, vowel detection and identification, and automatic spectral transition detection between phonemes within a word. The accuracy of both the V-UV-S and vowel recognition procedures is almost perfect. A broad phonetic segmentation procedure has been implemented, which exploits information from the above mentioned three procedures. Simple phonological constraints have been used to improve the accuracy of the segmentation process. The resultant sequence of labels are used for lexical access to retrieve the word or a small set of words sharing the same broad phonetic labelling. For the case of having more than one word-candidates, a verification procedure is used to choose the most likely one