The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We present a topic mixture language modeling approach making use of the soft classification notion of topic models. Given a text document set, we first perform document soft classification by applying a topic modeling process such as probabilistic latent semantic analyses (PLSA) or latent Dirichlet allocation (LDA) on the dataset. Then we can derive topic-specific n-gram counts from the classified...
We present a semi-supervised learning (SSL) method for building domain-specific language models (LMs) from general-domain data using probabilistic latent semantic analysis (PLSA). The proposed technique first performs topic decomposition (TD) on the combined dataset of domain-specific and general-domain data. Then it derives latent topic distribution of the interested domain, and derives domain-specific...
Natural language interface is an important research topic in the area of natural language processing (NLP). Natural language interaction with robot could be the most natural and efficient way. In order to build speech enabled human language interface of robots, our research goal is to study the problems in this area and develop technologies that can potentially improve human-robot interaction. In...
In this paper, we propose a subspace construction and selection strategy (SUBS) for speaker recognition with limited training and testing speech data. Based on the individual Gaussian distributions of Gaussian mixture model (GMM), each speaker's characteristic subspace is constructed by training an SVM using the corresponding Gaussian mean vectors from the GMMs of both enrollment and imposter speakers...
This paper presents a ??non-complicated?? automatic spoken language recognition system which can be effectively implemented using publicly available toolkits (such as HTK, SRILM and SVM-Light) and corpus resources (such as Switchboard, CallFriend, OHSU and NIST LRE07 speech corpora). This system involves two context-independent phone recognizers, a vector space modelling classifier and an equal weight...
This paper extends our previous work on feature transformation-based support vector machines for speaker recognition by proposing a joint MAP adaptation of feature transformation (FT) and Gaussian Mixture Models (GMM) parameters. In the new approach, the prior probability density functions (PDFs) of FT and GMM parameters are jointly estimated using the background data under the maximum likelihood...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.