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Parametric HMMs for Movement Recognition and Synthesis

MAGE is a library for realtime and interactive (reactive) parametric speech synthesis using hidden Markov models (HMMs).

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In humanoid robotics, the recognition and synthesis of parametric move-ments plays an extraordinary role for robot human interaction. Such a para-metric movement is a movement of a particular type (semantic), for example, similar pointing movements performed at different table-top positions. For understanding the whole meaning of a movement of a human, the recognition of its type, likewise its parameterization are important. Only both together convey the whole meaning. Vice versa, for mimicry, the synthesis of movements for the motor control of a robot needs to be parameterized, e.g., by the relative position a grasping action is performed at. For both cases, synthesis and recognition, only parametric approaches are meaningful as it is not feasible to store, or acquire all possible trajectories. In this paper, we use hidden Markov models (HMMs) extended in an exemplar-based parametric way (PHMM) to represent parametric movements. As HMMs are generative, they are well suited for synthesis as well as for recognition. Synthesis and recognition are carried out through interpolation of exemplar movements to generalize over the parameterization of a move-ment class. In the evaluation of the approach we concentrate on a systematical valida-tion for two parametric movements, grasping and pointing. Even though the movements are very similar in appearance our approach is able to distinguish the two movement types reasonable well. In further experiments, we show the applicability for online recognition based on very noisy 3D tracking data. The use of a parametric representation of movements is shown in a robot demo, where a robot removes objects from a table as demonstrated by an advisor. The synthesis for motor control is performed for arbitrary table-top positions. 1

One-model speech recognition and synthesis based on articulatory movement HMMs

recognition system. The "front-end" processing extracts a parametric representation or input pattern from the digitized input speech signal using the same types of techniques (e.g., linear predictive analysis or filter banks) that are used in speech analysis/synthesis systems. These acoustic features are designed to capture the linguistic features in a form that facilitates accurate linguistic decoding of the utterance. Cepstrum coefficients derived from either LPC parameters or spectral amplitudes derived from FFT or filter bank outputs are widely used as features (Rabiner and Juang, 1993). Such analysis techniques are often combined with vector quantization to provide a compact and effective feature representation. At the heart of a speech recognition system is the set of algorithms that compare the feature pattern representation of the input to members of a set of stored reference patterns that have been obtained by a training process. Equally important are algorithms for making a decision about the pattern to which the input is closest. Cepstrum distance measures are widely used for comparison of feature vectors, and dynamic time warping (DTW) and hidden Markov models (HMMs) have been shown to be very effective in dealing with the variability of speech (Rabiner and Juang, 1993). As shown in Figure 7, the most sophisticated systems also employ grammar and language models to aid in the decision process.

NIPS 2017 schedule - 2017 Conference

02/10/2017 · Parametric Hidden Markov Models for Recognition and Synthesis of Movements (2008)

In humanoid robotics, the recognition and synthesis of parametric movements plays an extraordinary role for robot human interaction. Such a parametric movement is a movement of a particular type (semantic), for example, similar pointing movements performed at different table-top positions. For understanding the whole meaning of a movement of a human, the recognition of its type, likewise its parameterization are important. Only both together convey the whole meaning. Vice versa, for mimicry, the synthesis of movements for the motor control of a robot needs to be parameterized, e.g., by the relative position a grasping action is performed at. For both cases, synthesis and recognition, only parametric approaches are meaningful as it is not feasible to store, or acquire all possible trajectories. In this paper, we use hidden Markov models (HMMs) extended in an exemplar-based parametric way (PHMM) to represent parametric movements. As HMMs are generative, they are well suited for synthesis as well as for recognition. Synthesis and recognition are carried out through interpolation of exemplar movements to generalize over the parameterization of a movement class. In the evaluation of the approach we concentrate on a systematical validation for two parametric movements, grasping and pointing. Even though the movements are very similar in appearance our approach is able to distinguish the two movement types reasonable well. In further experiments, we show the applicability for online recognition based on very noisy 3D tracking data. The use of a parametric representation of movements is shown in a robot demo, where a robot removes objects from a table as demonstrated by an advisor. The synthesis for motor control is performed for arbitrary table-top positions. 1

This paper focuses on speech modeling advances in continuous speech recognition, with an exposition of hidden Markov models (HMMs), the mathematical backbone behind these advances. While knowledge of properties of the speech signal and of speech perception have always played a role, recent improvements have relied largely on solid mathematical and probabilistic modeling methods, especially the use of HMMs for modeling speech sounds. These methods are capable of modeling time and spectral variability simultaneously, and the model parameters can be estimated automatically from given training speech data. The traditional processes of segmentation and labeling of speech sounds are now merged into a single probabilistic process that can optimize recognition accuracy.

NIPS 2017 – Accepted Papers - 2017 Conference

03/12/2008 · The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement

The analysis-by-synthesis paradigm of Figure 6 may also be useful for speech recognition applications. Indeed, if the block labeled "Model Parameter Generator" were a speech recognizer producing text or some symbolic representation as output, the block labeled "Speech Synthesis Model" could be a text-to-speech synthesizer. In this case the symbolic representation would be obtained as a by-product of the matching of the synthetic speech signal to the input signal. Such a scheme, although appealing in concept, clearly presents significant challenges. Obviously, the matching metric could not simply compare waveforms but would have to operate on a higher level. Defining a suitable metric and developing an appropriate optimization algorithm would require much creative research, and the implementation of such a system would challenge present computational resources.

The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement …
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