Phonotactics, dealing with permissible phone patterns and their frequencies of occurrence in a specific language, is acknowledged to be related to spoken language recognition (SLR). With the assistance of phone recognizers, each speech utterance can be decoded into an ordered sequence of phone vectors filled with likelihood scores contributed by all possible phone models. In this paper, we propose a novel approach to dig the concealed phonotactic structure out of the phone-likelihood vectors through a kind of multivariate time series analysis: dynamic linear models (DLM). In these models, treating the generation of phone patterns in each utterance as a dynamic system, the relationship between adjacent vectors is linearly and time-invariantly modeled, and unobserved states are introduced to capture a temporal coherence intrinsic in the system. Each utterance expressed by the DLM is further transformed into a fixed-dimensional linear subspace so that well-developed distance measures between two subspaces can be applied to linear discriminant analysis (LDA) in a dissimilarity-based fashion. The results of SLR experiments on the OGI-TS corpus demonstrate that the proposed framework outperforms the well-known vector space modeling (VSM)-based methods and achieves comparable performance to our previous subspace-based method.

This paper presents a novel subspace-based approach for phonotactic language recognition. The whole framework is divided into two parts: the speech feature representation and the subspacebased learning algorithm. First, the phonetic information as well as the contextual relationship, possessed by spoken utterances, are more abundantly retrieved by likelihood computation and feature concatenation through the decoding processed by an automatic speech recognizer. It is assumed that the extracted phone frames reside in a lower dimensional eigen-subspace, in which the structure of data can be approximately captured. Each utterance is further represented by a fixed-dimensional linear subspace. Second, to measure the similarity between two utterances, suitable non-Euclidean metrics are explored and applied to non-linear discriminant analysis in a kernel fashion, followed by a back-end classifier, such as the k-nearest neighbor (K-NN) classifier. The results of experiments on the OGI-TS database demonstrate that the proposed framework outperforms the well-known vector space modeling based method with relative reductions of 38.90% and 27.13% on the 1-to-50-second and 3-second data sets respectively in equal error rate (EER).

This paper presents a novel content-based query-by-tag music search system for an untagged music database. We design a new tag query interface that allows users to input multiple tags with multiple levels of preference (denoted as an MTML query) by colorizing desired tags in a web-based tag cloud interface. When a user clicks and holds the left mouse button (or presses and holds his/her finger on a touch screen) on a desired tag, the color of the tag will change cyclically according to a color map (from dark blue to bright red), which represents the level of preference (from 0 to 1). In this way, the user can easily organize and check the query of multiple tags with multiple levels of preference through the colored tags. To effect the MTML content-based music retrieval, we introduce a probabilistic fusion model (denoted as GMFM), which consists of two mixture models, namely a Gaussian mixture model and a multinomial mixture model. GMFM can jointly model the auditory features and tag labels of a song. Two indexing methods and their corresponding matching methods, namely pseudo song-based matching and tag affinity-based matching, are incorporated into the pre-learned GMFM. We evaluate the proposed system on the MajorMiner and CAL-500 datasets. The experimental results demonstrate the effectiveness of GMFM and the potential of using MTML queries to search music from an untagged music database.