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A new method is proposed for on-line handwriting recognition of Kanji characters. The method employs substroke HMMs as minimum units to constitute Japanese Kanji characters and utilizes the direction of pen motion. The main motivation is to fully utilize the continuous speech recognition algorithm by relating sentence speech to Kanji character, phonemes to substrokes, and grammar to Kanji structure. The proposed system consists input feature analysis, substroke HMMs, a character structure dictionary and a decoder. The present approach has the following advantages over the conventional methods that employ whole character HMMs. 1) Much smaller memory requirement for dictionary and models. 2) Fast recognition by employing efficient substroke network search. 3) Capability of recognizing characters not included in the training data if defined as a sequence of substrokes in the dictionary. 4) Capability of recognizing characters written by various different stroke orders with multiple definitions per one character in the dictionary. 5) Easiness in HMM adaptation to the user with a few sample character data.

This paper proposes a new class of hidden Markov model (HMM) called multiple-regression HMM (MR-HMM) that utilizes auxiliary features such as fundamental frequency (¢ ¤ £) and speaking styles that affect spectral parameters to better model the acoustic features of phonemes. Though such auxiliary features are considered to be the factors that degrade the performance of speech recognizers, the proposed MR-HMM adapts its model parameters, i.e. mean vectors of output probability distributions, depending on these auxiliary information to improve the recognition accuracy. Formulation for parameter reestimation of MR-HMM based on the EM algorithm is given in the paper. Experiments of speaker-dependent isolated word recognition demonstrated that MR-HMMs using ¢ £ based auxiliary features reduced the error rates by more than ¥ § ¦ © compared with the conventional HMMs.

Abstract. Previous work in speech-driven head motion synthesis is centred around Hidden Markov Model (HMM) based methods and data that does not show a large variability of expressiveness in both speech and motion. When using expressive data, these systems often fail to produce satisfactory results. Recent studies have shown that using deep neural networks (DNNs) results in a better synthesis of head motion, in particular when employing bidirectional long short-term memory (BLSTM). We present a novel approach which makes use of DNNs with stacked bottleneck features combined with a BLSTM architecture to model context and expressive variability. Our proposed DNN architecture outperforms conventional feed-forward DNNs and simple BLSTM networks in an objective evaluation. Results from a subjective evaluation show a significant improvement of the bottleneck architecture over feed-forward DNNs.