This paper provides a rough description of zero resource Query-by-Example retrieving system for the MediaEval 2013 spoken web search task. The proposed solution firstly implements the voice activity detection (VAD) utilizing variance of acceleration MFCC (VAMFCC) rule-based approach. A PCA-based segmentation, K-means clustering and GMM training are then used in order to built the posteriorgrams...

In this paper, a new strategy for a fast adaptation of acoustic models is proposed for embedded speech recognition. It relies on a general GMM, which represents the whole acoustic space, associated with a set of HMM state-dependent probability functions modeled as transformations of this GMM. The work presented here takes advantage of this architecture to propose a fast and efficient way to ada...

A hybrid Hidden Markov Model (HMM) Gaussian Mixture Model (GMM) system was proposed to automatically select tokens of /iau/, /ai/, /ei/, /m/ and /n/ in a database of recordings of Standard-Chinese speech collected under studio-clean, mobile-landline degraded and mismatched recording conditions. The FVC systems constructed were all MFCC GMM-UBM systems, but based on different portions of the rec...

Standard Gaussian mixture modelling does not possess time sequence information (TSI) other than that which might be embedded in the acoustic features. Dynamic time warping relates directly to TSI, time-warping two sequences of features into alignment. Here, a hybrid system embedding DTW into a GMM is presented. Improved automatic speaker verification performance is demonstrated. Testing 1000 sp...

One major source of performance decline in speaker recognition system is channel mismatch between training and testing. This paper focuses on improving channel robustness of speaker recognition system in two aspects of channel compensation technique and channel robust features. The system is text-independent speaker identification system based on two-stage recognition. In the aspect of channel ...

This paper describes content-based image retrieval techniques within the relevance feedback framework. The Gaussian mixture model (GMM) is used to characterize sub-class information to increase retrieval accuracy and reduce number of interactions during a query session. The implementation of GMM is based on the radial basis function using a new learning algorithm that can cope with small traini...

We discuss Dragon Systems’ approach to the NIST Speaker Recognition tasks. For the one-speaker task, we employ a combination of methods: a basic GMM system and two LVCSR-based systems, one using standard mixture models and the other using nonparametric techniques. We discuss some explorations of the recently introduced two-speaker tasks based on the GMM system alone. “Cheating” tests using NIST...

In this paper an attempt is made to automatically recognize the speaker’s accent among regional Swiss French accents from four different regions of Switzerland, i.e. Geneva (GE), Martigny (MA), Neuchâtel (NE) and Nyon (NY). To achieve this goal, we rely on a generative probabilistic framework for classification based on Gaussian mixture modelling (GMM). Two different GMM-based algorithms are in...

With a dramatic increase in the number and variety of applications running over the internet, it is very important to be capable of dynamically identifying and classifying flows/traffic according to their network applications. Meanwhile, internet application classification is fundamental to numerous network activities. In this paper, we present a novel methodology for identifying different inte...

This paper gives an overview of acoustic modeling and search techniques for low-power embedded ASR decoders. Our design decisions prioritize memory bandwidth, which is the main driver in system power consumption. We evaluate three acoustic modeling approaches–Gaussian mixture model (GMM), subspace GMM (SGMM) and deep neural network (DNN)–and identify tradeoffs between memory bandwidth and recog...