Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization.

ABSTRACT: Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat software was used, and different data mining algorithms were applied using Weka software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization

Acoustic-phonetic Features For Refining The Explicit Speech Segmentation

This paper describes the refinement of the automatic speech segmentation into phones obtained via Hidden Markov Models (HMM). This refinement is based on acoustic-phonetic features associated to different phone classes. The proposed system was evaluated using both a small speaker dependent Brazilian Portuguese speech database and a speaker independent speech database (TIMIT). The refinement was applied to the boundaries obtained by just running the Viterbi's algorithm on the HMMs associated to the different utterances. Improvements of 30% and 13% were achieved in the percentage of segmentation errors below 20 ms for the speaker dependent and speaker independent databases respectively.318531856Vidal, E., Marzal, A., A review and new approaches for automatic segmentation of speech signals (1990) Signal Processing V: Theories and Applications, pp. 43-53. , L. Torres, E. Masgrau and M. A. Lagunas eds, Elsevier Science Publishers B. V, ppToledano, D., Gómez, L.A., Grande, L.V., Automatic Phonetic Segmentation (2003) IEEE Transactions on Speech and Audio Processing, 11 (6). , Novembervan Hemert, J.P., Automatic Segmentation of Speech (1991) IEEE Transactions on Signal Processing, 39 (4), pp. 1008-1012. , AprilSaiJayram, A.K.V., Ramasubramanian, V., Sreenivas, T.V., Robust parameters for automatic segmentation of speech (2002) Proceedings of Acoustics, Speech and Signals Processing, 1, pp. 513-512Hatazaki, K., Komor, Y., Kawabata, T., Shikano, K., Phoneme segmentation using spectrogram reading knowledge (1989) Proceeding of the International Conference on Acoustics Speech and Signal Processing, pp. 393-396Selmini, A. M. and Violaro, F. Improving the Explicit Automatic Speech Segmentation Provided by HMMs, Proceedings of the International Workshop on Telecommunications, pp. 220-226, Santa Rita do Sapucaí, Brazil, 2007Wang, L., Zhao, Y., Chu, M., Zhou, J., Cao, Z., Refining segmental boundaries for TTS database using fine contextual-dependent boundary models (2004) Proceedings of the ICASP, 1, pp. 641-644. , Beijing, China, MayDemuynck, K., Laureys, T., A comparison of different approaches to automatic speech segmentation (2002) Proceedings of the 5th International Conference on Text, Speech and Dialogue, , Brno, Czech Republic, SeptemberJuneja, A., Speech Recognition Based on Phonetic Features and Acoustic Landmarks (2004), PhD Thesis, University of Maryland, College Park, USAHosom, J.P., Automatic Phoneme Alignment Based on Acoustic-Phonetic Modeling (2002) 7th International Conference on Spoken Language Processing, , Denver, CO, USA, Septembe

A hybrid model for text-to-speech synthesis

This paper describes a hybrid model developed for high-quality, concatenation-based, text-to-speech synthesis. The speech signal is submitted to a pitch-synchronous analysis and decomposed into a harmonic component, with a variable maximum frequency, plus a noise component. The harmonic component is modeled as a sum of sinusoids with frequencies multiple of the pitch. The noise component is modeled as a random excitation applied to an LPC filter. In unvoiced segments, the harmonic component is made equal to zero. In the presence of pitch modifications, a new set of harmonic parameters is evaluated by resampling the spectrum envelope at the new harmonic frequencies. For the synthesis of the harmonic component in the presence of duration and/or pitch modifications, a phase correction is introduced into the harmonic parameters. The sinusoidal model of synthesis is used for the harmonic component and the LPC model combined with an overlap and add procedure is used for the noise synthesis. This hybrid model enables independent and continuous control of duration and pitch of the synthesized speech. Comparative evaluation tests made in a text-to-speech environment have shown that the hybrid model assures better performance than the time-domain pitch-synchronous overlap-add (TD-PSOLA) model.6542643

A Large Speech Database For Brazilian Portuguese Spoken Language Research

Speech recognition systems use statistical methods based algorithms, and therefore need several training samples to perform properly. Consequently such systems require huge databases for training and testing. The development of large speech corpora in Europe and in the USA was possible only with the cooperation among research centers, universities, private companies and the government. In these countries, the availability of such databases provided the resources which were responsible for the great improvement in speech technologies in the last few years. In Brazil, such consortiums are not even mentioned, and the researchers have to work with small, locally developed databases. In this article we report an effort to develop a large speech corpus for Brazilian Portuguese to fill this crucial gap.272119319

Facial animation based on context-dependent visemes

This paper presents a novel approach for the generation of realistic speech synchronized 3D facial animation that copes with anticipatory and perseveratory coarticulation. The methodology is based on the measurement of 3D trajectories of fiduciary points marked on the face of a real speaker during the speech production of CVCV non-sense words. The trajectories are measured from standard video sequences using stereo vision photogrammetric techniques. The first stationary point of each trajectory associated with a phonetic segment is selected as its articulatory target. By clustering according to geometric similarity all articulatory targets of a same segment in different phonetic contexts, a set of phonetic context-dependent visemes accounting for coarticulation is identified. These visemes are then used to drive a set of geometric transformation/deformation models that reproduce the rotation and translation of the temporomandibular joint on the 3D virtual face, as well as the behavior of the lips, such as protrusion, and opening width and height of the natural articulation. This approach is being used to generate 3D speech synchronized animation from both natural and synthetic speech generated by a text-to-speech synthesizer. (c) 2006 Elsevier Ltd. All rights reserved.30697198

Analysis Of The Multifractal Nature Of Speech Signals

Frame duration is an essential parameter to ensure correct application of multifractal signal processing. This paper aims to identify the multifractal nature of speech signals through theoretical study and experimental verification. One important part of this pursuit is to select adequate ranges of frame duration that effectively display evidence of multifractal nature. An overview of multifractal theory is given, including definitions and methods for analyzing and estimating multifractal characteristics and behavior. Based on these methods, we evaluate the utterances from two different Portuguese speech databases by studying their singularity curves (τ(q) and f(α)).We conclude that the frame duration between 50 and 100 ms is more suitable and useful for multifractal speech signal processing in terms of speaker recognition performance [11]. © 2012 Springer-Verlag.7441 LNCS740748Campell, J., Speaker Recognition: A Tutorial (1998) Proceeding of the IEEE, 85 (9)Reynolds, D.A., Rose, R.C., Robust Text-Independent Speaker Identification Using Mixture Speaker Model (1995) IEEE Trans. Speech Audio Processing, 3 (1), pp. 72-82Langi, A., Kinsner, W., Consonant Characterization Using Correlation Fractal Dimension for Speech Recognition (1995) Proc. on IEEE Western Canada Conference on Communications, Computer, and Power in the Modem Environment, Winnipeg, Canada, 1, pp. 208-213Jayant, N., Noll, P., (1984) Digital Coding of Waveforms: Principles and Applications to Speech and Video, p. 688. , Prentice-Hall, Englewood CliffsSant'Ana, R., Coelho, R., Alcaim, A., Text-Independent Speaker Recognition Based on the Hurst Parameter and the Multidimensional Fractional Brownian Motion Model (2006) IEEE Trans. on Audio, Speech, and Language Processing, 14 (3), pp. 931-940Zhou, Y., Wang, J., Zhang, X., Research on Speaker Recognition Based on Multifractal Spectrum Feature (2010) Second International Conference on Computer Modeling and Simulation, pp. 463-466Maragos, P., Fractal Aspects of Speech Signals: Dimension and Interpolation (1991) Proc. IEEE ICASSP, 1, pp. 417-420Langitt, A., Soemintapurat, K., Kinsners, W., Multifractal Processing of Speech Signals Information, Communications and Signal Processing (1997) LNCS, 1334, pp. 527-531. , Han, Y., Quing, S. (eds.) ICICS 1997. Springer, HeidelbergKinsner, W., Grieder, W., Speech Segmentation Using Multifractal Measures and Amplification of Signal Features (2008) Proc. 7th IEEE Int. Conf. on Cognitive Informatics (ICCI 2008), pp. 351-357Adeyemi, O.A., Multifractal Analysis of Unvoiced Speech Signals (1997) ETD Collection for University of Rhode Island. Paper AAI9805227González, D.C., Lee, L.L., Violaro, F., (2011) Use of Multifractal Parameters for Speaker Recognition, , M. Eng. thesis, FEEC/UNCAMP, Campinas, BrazilSténico, J.W., Lee, L.L., (2009) Estimation of Loss Probability and An Admission Control Scheme for Multifractal Network Traffic, , M. Eng. thesis, FEEC/UNCAMP, Campinas, BrazilRiedi, R.H., Crouse, M.S., Ribeiro, V.J., Baraniuk, R.G., A Multifractal Wavelet Model with Application to Network Traffic (1999) IEEE Trans. on Information Theory, 45 (3), pp. 992-1018Krishna, M.P., Gadre, V.M., Dessay, U.B., (2003) Multifractal Based Network Traffic Modeling, , Kluwer Academic Publishers., Ed. BombayYnoguti, C., Violaro, F., (1999) Continuous Speech Recognition Using Hidden Markov Models, , D. Eng. thesis, FEEC/UNCAMP, Campinas, BrazilHolmes, J., Holmes, W., (2001) Speech Synthesis and Recognition, , 2nd edn. Tayor & Francis, LondonResearch Center INRIA Saclay, , http://fraclab.saclay.inria.fr

Vocalization As A Tool For Identifying The Level Of Stress In Piglets

Amongst the challenges in pig production under today's competitive market the issue of meeting the standards of animal welfare is enhanced. Animal vocalization can be a useful tool to identify the level of stress in piglets. This research aimed to evaluate the vocalization of piglets during a surgical procedure with and without anesthesia, and assess their level of stress by analyzing their vocal expressions. Two level of stress were assessed: moderate stress and acute stress. No difference was found the level of pain in both treatments.165168Cordeiro, A.F.S., Pereira, E.M., Nääs, I.A., Silva, W.T., Moura, D.J., Medida de vocalização de suínos (Sus scrofa) como um indicador de gasto energético. (abstract in English) (2009) Revista Brasileira de Engenharia de Biosistemas, 2, pp. 143-152Leidig, M.S., Hertrampf, B., Failing, K., Schumann, A., Reiner, G., Pain and discomfort in male piglets during surgical castration with and without local anesthesia as determined by vocalization and defense behaviour (2009) Applied Animal Behaviour Science, 116, pp. 174-178Manteuffel, G., Schon, P.C., Measuring pig welfare by automatic monitoring of stress calls (2004) Bornier Agratechnische Bericht, 29, pp. 110-118Marx, G., Horn, T., Thielebein, J., Knubel, B., Borell, E., Analysis of pain-related vocalization in young pigs (2003) Journal of Sound and Vibration, 266, pp. 687-698Schrader, L., Todt, D., Vocal quality is correlated with levels of stress hormones in domestic pigs (1998) Etology, 104, pp. 859-876Weary, D.M., Fraser, D., Vocal response of piglet to weaning: Effect of piglet age (1997) Applied Animal Behaviour Science, 54, pp. 153-16