Predictive Interfaces for Long-Distance Tele-Operations

We address the development of predictive tele-operator interfaces for humanoid robots with respect to two basic challenges. Firstly, we address automating the transition from fully tele-operated systems towards degrees of autonomy. Secondly, we develop compensation for the time-delay that exists when sending telemetry data from a remote operation point to robots located at low earth orbit and beyond. Humanoid robots have a great advantage over other robotic platforms for use in space-based construction and maintenance because they can use the same tools as astronauts do. The major disadvantage is that they are difficult to control due to the large number of degrees of freedom, which makes it difficult to synthesize autonomous behaviors using conventional means. We are working with the NASA Johnson Space Center's Robonaut which is an anthropomorphic robot with fully articulated hands, arms, and neck. We have trained hidden Markov models that make use of the command data, sensory streams, and other relevant data sources to predict a tele-operator's intent. This allows us to achieve subgoal level commanding without the use of predefined command dictionaries, and to create sub-goal autonomy via sequence generation from generative models. Our method works as a means to incrementally transition from manual tele-operation to semi-autonomous, supervised operation. The multi-agent laboratory experiments conducted by Ambrose et. al. have shown that it is feasible to directly tele-operate multiple Robonauts with humans to perform complex tasks such as truss assembly. However, once a time-delay is introduced into the system, the rate of tele\ioperation slows down to mimic a bump and wait type of activity. We would like to maintain the same interface to the operator despite time-delays. To this end, we are developing an interface which will allow for us to predict the intentions of the operator while interacting with a 3D virtual representation of the expected state of the robot. The predictive interface anticipates the intention of the operator, and then uses this prediction to initiate appropriate sub-goal autonomy tasks

Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition

A theoretical framework for Bayesian adaptive training of the parameters of a discrete hidden Markov model (DHMM) and of a semi-continuous HMM (SCHMM) with Gaussian mixture state observation densities is presented. In addition to formulating the forward-backward MAP (maximum a posteriori) and the segmental MAP algorithms for estimating the above HMM parameters, a computationally efficient segmental quasi-Bayes algorithm for estimating the state-specific mixture coefficients in SCHMM is developed. For estimating the parameters of the prior densities, a new empirical Bayes method based on the moment estimates is also proposed. The MAP algorithms and the prior parameter specification are directly applicable to training speaker adaptive HMMs. Practical issues related to the use of the proposed techniques for HMM-based speaker adaptation are studied. The proposed MAP algorithms are shown to be effective especially in the cases in which the training or adaptation data are limited.published_or_final_versio

Grapheme-based Automatic Speech Recognition using Probabilistic Lexical Modeling

Automatic speech recognition (ASR) systems incorporate expert knowledge of language or the linguistic expertise through the use of phone pronunciation lexicon (or dictionary) where each word is associated with a sequence of phones. The creation of phone pronunciation lexicon for a new language or domain is costly as it requires linguistic expertise, and includes time and money. In this thesis, we focus on effective building of ASR systems in the absence of linguistic expertise for a new domain or language. Particularly, we consider graphemes as alternate subword units for speech recognition. In a grapheme lexicon, pronunciation of a word is derived from its orthography. However, modeling graphemes for speech recognition is a challenging task for two reasons. Firstly, grapheme-to-phoneme (G2P) relationship can be ambiguous as languages continue to evolve after their spelling has been standardized. Secondly, as elucidated in this thesis, typically ASR systems directly model the relationship between graphemes and acoustic features; and the acoustic features depict the envelope of speech, which is related to phones. In this thesis, a grapheme-based ASR approach is proposed where the modeling of the relationship between graphemes and acoustic features is factored through a latent variable into two models, namely, acoustic model and lexical model. In the acoustic model the relationship between latent variables and acoustic features is modeled, while in the lexical model a probabilistic relationship between latent variables and graphemes is modeled. We refer to the proposed approach as probabilistic lexical modeling based ASR. In the thesis we show that the latent variables can be phones or multilingual phones or clustered context-dependent subword units; and an acoustic model can be trained on domain-independent or language-independent resources. The lexical model is trained on transcribed speech data from the target domain or language. In doing so, the parameters of the lexical model capture a probabilistic relationship between graphemes and phones. In the proposed grapheme-based ASR approach, lexicon learning is implicitly integrated as a phase in ASR system training as opposed to the conventional approach where first phone pronunciation lexicon is developed and then a phone-based ASR system is trained. The potential and the efficacy of the proposed approach is demonstrated through experiments and comparisons with other standard approaches on ASR for resource rich languages, nonnative and accented speech, under-resourced languages, and minority languages. The studies revealed that the proposed framework is particularly suitable when the task is challenged by the lack of both linguistic expertise and transcribed data. Furthermore, our investigations also showed that standard ASR approaches in which the lexical model is deterministic are more suitable for phones than graphemes, while probabilistic lexical model based ASR approach is suitable for both. Finally, we show that the captured grapheme-to-phoneme relationship can be exploited to perform acoustic data-driven G2P conversion

Aportación a la extracción paramétrica en reconocimiento de voz robusto basada en la aplicación de conocimiento de fonética acústica

This thesis is based on the following hypothesis: the introduction of direct knowledge from the acoustic-phonetic field to the speech recognition problem, especially in the feature extraction step, may constitute a solid base of analysis for the determination of the behavior and capabilities of those systems and their improvement, as well. Most of the complexity of this Ph.D. thesis comes from the different subjects related with the speech processing área. The application of acoustic-phonetic information to the speech recognition research área implies a deep knowledge of both subjects. The research carried out in this work has been divided in two main parts: analysis of the current feature extraction methods and a study of several possible procedures about the incorporation of phonetic-acoustic knowledge to those systems. Abundant recognition and related quality measure results are presented for 50 different parameter extraction models. Details about the real-time implementation on a DSP platform (TMS3230C31-60) of two different parameter extraction models are presented. Finally, a set of computer tools developed for building and testing new speech recognition systems has been produced. Besides, the application of several results from this work can be extended to other speech processing áreas, such as computer assisted language learning, linguistic rehabilitation, etc.---ABSTRACT---La hipótesis en la que se basa el desarrollo de esta tesis, se centra en la suposición de que la aportación de conocimiento directo, proveniente del campo de la fonética acústica, al problema del reconocimiento automático de la voz, en concreto a la etapa de extracción de características, puede constituir una base sólida con la que poder analizar el comportamiento y capacidad de discriminación de dichos sistemas, así como una forma de mejorar sus prestaciones. Parte de la complejidad que presenta esta tesis doctoral, viene motivada por las diferentes disciplinas que están relacionadas con el área de procesamiento de la voz. La aplicación de información fonética-acústica al campo de investigación del reconocimiento del habla requiere un amplio conocimiento de ambas materias. Las investigaciones desarrolladas en este trabajo se han dividido en dos bloques fundamentales: análisis de los métodos actuales de extracción de rasgos fonéticos y un estudio de algunas posibles formas de incorporación de conocimiento fonético-acústico a dichos sistemas. En esta tesis se ofrecen abundantes resultados relativos a tasas de reconocimiento y medidas acerca de la calidad de este proceso, para un total de 50 modelos de extracción de parámetros. Así mismo se incluyen los detalles de la implementación en tiempo real para una plataforma DSP, en concreto TMS320C31-60, de dos diferentes modelos de extracción de rasgos. Además, se ha desarrollado un conjunto de las herramientas informáticas que pueden servir de base para construir y validar de forma sencilla, nuevos sistemas de reconocimiento. La aplicación de algunos de los resultados del trabajo puede extenderse también a otras áreas del tratamiento de la voz, tales como la enseñanza de una segunda lengua, logopedia, etc

Signal processing and acoustic modelling of speech signals for speech recognition systems

Natural man-machine interaction is currently one of the most unfulfilled pledges of automatic speech recognition (ASR). The purpose of an automatic speech recognition system is to accurately transcribe or execute what has been said. State-of-the-art speech recognition systems consist of four basic modules: the signal processing, the acoustic modelling, the language modelling, and the search engine. The subject of this thesis is the signal processing and acoustic modelling modules. We pursue the modelling of spoken signals in an optimum way. The resultant modules can be used successfully for the subsequent two modules. Since the first order hidden Markov model (HMM) has been a tremendously successful mathematically established paradigm, which makes it the up-to-the-minute technique in current speech recognition systems, this dissertation bases all its studies and experiments on HMM. HMM is a statistical framework that supports both acoustic and temporal modelling. It is widely used despite making a number of suboptimal modelling assumptions, which put limits on its full potential. We investigate how the model design strategy and the algorithms can be adapted to HMMs. Large suites of experimental results are demonstrated to expound the relative effectiveness of each component within the HMM paradigm. This dissertation presents several strategies for improving the overall performance of baseline speech recognition systems. The implementation of these strategies was optimised in a series of experiments. We also investigate selecting the optimal feature sets for speech recognition improvement. Moreover, the reliability of human speech recognition is attributed to the specific properties of the auditory presentation of speech. Thus, in this dissertation, we explore the use of perceptually inspired signal processing strategies, such as critical band frequency analysis. The resulting speech representation called Gammatone cepstral coefficients (GTCC) provides relative improvement over the baseline recogniser. We also investigate multiple signal representations for recognition in an ASR to improve the recognition rate. Additionally, we developed fast techniques that are useful for evaluation and comparison procedures between different signal processing paradigms. The following list gives the main contributions of this dissertation: • Speech/background discrimination. • HMM initialisation techniques. • Multiple signal representation with multi-stream paradigms. • Gender based modelling. • Feature vectors dimensionality reduction. • Perceptually motivated feature sets. • ASR training and recognition packages for research and development. Many of these methods can be applied in practical applications. The proposed techniques can be used directly in more complicated speech recognition systems by introducing their resultants to the language and search engine modules.UnpublishedAbdulla, W. H. and N. K. Kasabov (1999a). Speech recognition enhancement via robust CHMM speech background discrimination. Proc. ICONIP/ANZIIS/ANNES'99 International Workshop, New Zealand. Abdulla, W. H. and N. K. Kasabov (1999b). Two pass hidden Markov model for speech recognition systems. Proc. ICICS'99, Singapore. Abdulla, W. H. and N. K. Kasabov (1999c). The concepts of hidden Markov model in speech recognition. IJCNN'99, N. K. Kasabov, W. H. Abdulla (Ed.), Washington, DC, July 10-16, Chapter 4. Abdulla, W. H. and N. K. Kasabov (2000). Feature selection for parallel CHMM speech recognition systems. Proc. of the Fifth Joint Conference on Information Sciences, vol.2, pp 874-878, Atlantic City, New Jersey, USA. Abdulla, W. H. and N. K. Kasabov (2001). 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Speech Recognition: Invited paper presented at the 1974 IEEE symposium. New York, Academic Press. Barnwell-III, T. P. (1980). A comparison of parametrically different objective speech quality measures using correlation analysis with subjective quality results. Proc. IEEE ICASSP'80, Denver. Bateman, D. C., D. K. Bye and M. J. Hunt (1992). Spectral contrast normalization and other techniques for speech recognition in noise. Proc. IEEE ICASSP'92, San Francisco, USA. Baum, L. E. (1972). "An inequality and associated miximization technique in statistical estimation for probabilistic functions of Markov processe." Proc. Symp. On Inequalities 3: 1-7. Baum, L. E. and J. A. Egon (1967). "An inequality with applications to statistical estimation for probabilistic functions of Markov process and to a model for ecology." Bull. Amer. Meteorol. Soc. 73: 360-363. Baum, L. E. and T. Petrie (1966). "Statistical inference for probabilistic functions of finite state Markov chains." Ann. Math. Stat. 37: 1554-1563. Baum, L. E., T. Petrie, G. Soules and N. Weiss (1970). "A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains." Annals of Mathematical Statistics 41(1): 164-171. Becchetti, C. and L. P. Ricotti (1999). Speech recognition theory and C++ implementation, John Wiley & Sons. Bellegarda, J. and D. Nahamoo (1989). Tied mixture continuous parameter models for large vocabulary isolated speech recognition. Proc. ICASSP'89, Glasgow, Scotland. Bellegarda, J. and D. Naharnoo (1990). "Tied mixture continuous parameter modeling for speech recognition." IEEE Trans. ASSP 38(12): 2033-2045. Bengio, S. and Y. Bengio (2000a). "Taking on the curse of dimentionality in joint distributions using neural networks." IEEE Trans. Neural Networks 11(3): 550-557. Bengio, Y. (1996). Neural Networks for Speech and Sequence Processing, International Thomson Computer Press. Bengio, Y. and S. Bengio (2000b). Modeling high-dimensional discrete data with multi-layer neural networks. Advances in Neural Information Processing Systems 12. S. A. Jolla, T. K. Leen and K.-R. Miler, MIT Press: 400-406. Bin, J., T. Calhurst, A. EI-Jaroudi, R. lyer, et al. (1999). Recent experiments in large vocabulary conversational speech recognition. Proc. IEEE ICASSP'99, Phoenix. Blimes, J. A. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Berkeley, CA, International Computer Science Institute. Bocchieri, E. and B. Mak (1997). Subspace distribution clustering for continuous observation density hidden Markov models. Proc. Eurospeech. Boll, S. F. (1979). "Suppression of acoustic noise in speech using spectral subtraction." IEEE Trans. ASSP 27(2): 113-120. Bou-Ghazale, S. E. and A. 0. Asadi (2000). Hands-free voice activation of personal communication devices. Proc. IEEE ICASSP'2000, Istanbul, Turkey. Bourlard, H., S. Bengio and K. Weber (2001). New approaches towards robust and adaptive speech recognition. Advances in Neural Information Processing Systems 13. T. K. Leen, T. G. Dietterich and V. Tresp, MIT Press: 751-757. Bourlard, H., S. Dupont and C. Ris (1996). Multi-stream speech recognition. Bourlard, H. and N. Morgan (1993). Connectionist Speech Recognition. A Hybrid Approach. Boston, Kluwer Academic Publishers. Bourlard, H. and N. Morgan (1994). Connectionist Speech Recognition, Kluwer Academic Publishers. Bourlard, H., C. J. Wellekens and H. Ney (1984). Connected digit recognition using vector quantization. Proc. IEEE ICASSP'84, San Diego, USA. Burton, D. K. and J. E. Shore (1985). "Speaker-dependent isolated word recognition using speaker-independent vector quantization coodbooks augmented with speaker-specific data." IEEE Trans. ASSP 33(2): 440-443. Chan, A. K. and S. J. Liu (1998). Wavelet Toolware: Software for Wavelet Training, Academic Press. Chen, S. S. and R. A. Gopinath (2001). 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"On cochlea encoding: Potentialities and limitations of the reverse-correlation technique." J. Acoust. Soc. Am. 63(1): 115- 135. de-Boer, E. and P. Kuyper (1968). "Triggered correlation." IEEE Trans. Biomed. Eng. BME-15: 169 - 179. DeIler, J. R., J. G. Proakis and J. H. Hansen (1993). Discrete-Time Processing of Speech Signals. New York, Macmillan Publishing. Dempster, A. P., N. M. Laird and D. B. Rubin (1977). "Maximum likelihood from incomplete data via the EM algorithm." J. Royal Statist. Soc. Ser. B 39(1): 1-38. Devijver, P. A. and J. Kittler (1982). Pattern Recognition: A Statistical Approach. Englewood Cliffs, NJ, Prentice-Hall. Donoho, D. L., Ed. (1993). Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data. Proceedings of the Symposia in Applied Mathematics, American Mathematical Society. Donoho, D. L. (1995). "Denoising by soft-thresholding." IEEE Trans. IT 41(3): 613-627. Donoho, D. L. and I. M. Johnstone (1994). 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Signal processing and acoustic modelling of speech signals for speech recognition systems

Natural man-machine interaction is currently one of the most unfulfilled pledges of automatic speech recognition (ASR). The purpose of an automatic speech recognition system is to accurately transcribe or execute what has been said. State-of-the-art speech recognition systems consist of four basic modules: the signal processing, the acoustic modelling, the language modelling, and the search engine. The subject of this thesis is the signal processing and acoustic modelling modules. We pursue the modelling of spoken signals in an optimum way. The resultant modules can be used successfully for the subsequent two modules. Since the first order hidden Markov model (HMM) has been a tremendously successful mathematically established paradigm, which makes it the up-to-the-minute technique in current speech recognition systems, this dissertation bases all its studies and experiments on HMM. HMM is a statistical framework that supports both acoustic and temporal modelling. It is widely used despite making a number of suboptimal modelling assumptions, which put limits on its full potential. We investigate how the model design strategy and the algorithms can be adapted to HMMs. Large suites of experimental results are demonstrated to expound the relative effectiveness of each component within the HMM paradigm. This dissertation presents several strategies for improving the overall performance of baseline speech recognition systems. The implementation of these strategies was optimised in a series of experiments. We also investigate selecting the optimal feature sets for speech recognition improvement. Moreover, the reliability of human speech recognition is attributed to the specific properties of the auditory presentation of speech. Thus, in this dissertation, we explore the use of perceptually inspired signal processing strategies, such as critical band frequency analysis. The resulting speech representation called Gammatone cepstral coefficients (GTCC) provides relative improvement over the baseline recogniser. We also investigate multiple signal representations for recognition in an ASR to improve the recognition rate. Additionally, we developed fast techniques that are useful for evaluation and comparison procedures between different signal processing paradigms. The following list gives the main contributions of this dissertation: • Speech/background discrimination. • HMM initialisation techniques. • Multiple signal representation with multi-stream paradigms. • Gender based modelling. • Feature vectors dimensionality reduction. • Perceptually motivated feature sets. • ASR training and recognition packages for research and development. Many of these methods can be applied in practical applications. 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