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This paper describes the TBNR system, which features at many state-of-the-art technologies of speech recognition, covering the decoder, acoustic modeling, speech recognition features, and etc. By integrating these technologies, several optimizations have been performed to utilize multi-processors resources. Along with models in several typical languages, these systems could be used at once for several...
This paper reports our recent work on optimizing the AF (articulatory features) based confidence measures, and combining them with the traditional HMM-based confidence measures. Different articulatory properties are analyzed using a separate AF-based confidence calculation method proposed in this paper, and are observed to be both complementary and redundant. A more compact subset is chosen and assembled...
Sign language recognition systems suffer from the problem of signer dependence. In this letter, we propose a novel method that adapts the original model set to a specific signer with his/her small amount of training data. First, affinity propagation is used to extract the exemplars of signer independent hidden Markov models; then the adaptive training vocabulary can be automatically formed. Based...
In this paper, we propose a speech emotion recognition system using both spectral and prosodic features. Most traditional systems have focused on spectral features or prosodic features. Since both the spectral and the prosodic features contain emotion information, it is believed that the combining of spectral features and prosodic features will improve the performance of the emotion recognition system...
This paper tries to deal with the problem of performance degradation in emotion affected speech recognition. The F-ratio analysis method in statistics is utilized to analyze the significance of different frequency bands for speech unit classification. The result is then used to optimize filter bank design for Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) features...
In this paper, one fuzzy retrieval algorithm is designed to work with LVCSR in the speech navigation system. Inverted indexing as well as other searching skills are utilized to speed up the searching while keeping the performance. Several cell levels are tried instead of just using word. Easily reaching 90% sentence accuracy within normal database, this framework can also handle very large database,...
In this paper, we present an approach that using articulatory features (AFs) derived from spectral features for speech emotion recognition. Also, we investigated the combination of AFs and spectral features. Systems based on AFs only and combined spectral-articulatory features are tested on the CASIA Mandarin emotional corpus. Experiments results show that AFs alone are not suitable for speech emotion...
In this study, some research activities on expressive speech recognition and conversion will be introduced. A database consisting of five kinds of speech emotions (i.e. happiness, sadness, surprise, anger and neutral) is used. Not only those traditional features such as mfcc, plp, and pitch are studied, but also a new feature extraction method based on fisher's F-Ratio is proposed and reported. In...
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