Microphone arrays allow to exploit the spatial coherence between simultaneously recorded microphone signals, e.g., to perform speech enhancement, i.e., to extract a speech signal and reduce background noise. However, in systems where the microphones are not sampled in a synchronous fashion, as it is often the case in wireless acoustic sensor networks, a sampling rate offset (SRO) exists between signals recorded in different nodes, which severely affects the speech enhancement performance. To avoid this performance reduction, the SRO should be estimated and compensated for. In this paper, we propose a new approach to blind SRO estimation for an asynchronous wireless acoustic sensor network, which exploits the phase drift of the coherence between the asynchronous microphones signals. We utilize the fact that the SRO causes a linearly increasing time delay between two signals and hence a linearly increasing phase-shift in the short-time Fourier transform domain. The increasing phase shift, observed as a phase drift of the coherence between the signals, is used in a weighted least-squares framework to estimate the SRO. This method is referred to as least-squares coherence drift (LCD). Experimental results in different real-world recording and simulated scenarios show the effectiveness of LCD compared to different benchmark methods. The LCD is effective even for short signal segments. We finally demonstrate that the use of the LCD within a conventional compensation approach eliminates the performance loss due to SRO in a speech enhancement algorithm based on the multichannel Wiener filter.

We study a distributed node-specific parameter estimation problem where each node in a wireless sensor network is interested in the simultaneous estimation of different vectors of parameters that can be of local interest, of common interest to a subset of nodes, or of global interest to the whole network. We assume a setting where the nodes do not know which other nodes share the same estimation interests. First, we conduct a theoretical analysis on the asymptotic bias that results in case the nodes blindly process all the local estimates of all their neighbors to solve their own node-specific parameter estimation problem. Next, we propose an unsupervised diffusion-based LMS algorithm that allows each node to obtain unbiased estimates of its node-specific vector of parameters by continuously identifying which of the neighboring local estimates correspond to each of its own estimation tasks. Finally, simulation experiments illustrate the efficiency of the proposed strategy.

Recent studies show that Gaussian mixture model (GMM) weights carry less, yet complimentary, information to GMM means for language and dialect recognition. However, state-of-the-art language recognition systems usually do not use this information. In this research, a non-negative factor analysis (NFA) approach is developed for GMM weight decomposition and adaptation. This modeling, which is conceptually simple and computationally inexpensive, suggests a new low-dimensional utterance representation method using a factor analysis similar to that of the i-vector framework. The obtained subspace vectors are then applied in conjunction with i-vectors to the language/dialect recognition problem. The suggested approach is evaluated on the NIST 2011 and RATS language recognition evaluation (LRE) corpora and on the QCRI Arabic dialect recognition evaluation (DRE) corpus. The assessment results show that the proposed adaptation method yields more accurate recognition results compared to three conventional weight adaptation approaches, namely maximum likelihood re-estimation, non-negative matrix factorization, and a subspace multinomial model. Experimental results also show that the intermediate-level fusion of i-vectors and NFA subspace vectors improves the performance of the state-of-the-art i-vector framework especially for the case of short utterances.

In this paper, three utterance modelling approaches, namely Gaussian Mean Supervector (GMS), i-vector and Gaussian Posterior Probability Supervector (GPPS), are applied to the accent recognition problem. For each utterance modeling method, three different classifiers, namely the Support Vector Machine (SVM), the Naive Bayesian Classifier (NBC) and the Sparse Representation Classifier (SRC), are employed to find out suitable matches between the utterance modelling schemes and the classifiers. The evaluation database is formed by using English utterances of speakers whose native languages are Russian, Hindi, American English, Thai, Vietnamese and Cantonese. These utterances are drawn from the National Institute of Standards and Technology (NIST) 2008 Speaker Recognition Evaluation (SRE) database. The study results show that GPPS and i-vector are more effective than GMS in this accent recognition task. It is also concluded that among the employed classifiers, the best matches for i-vector and GPPS are SVM and SRC, respectively.

Motivated by the success of i-vectors in the field of speaker recognition, this paper proposes a new approach for age estimation from telephone speech patterns based on i-vectors. In this method, each utterance is modeled by its corresponding i-vector. Then, Support Vector Regression (SVR) is applied to estimate the age of speakers. The proposed method is trained and tested on telephone conversations of the National Institute for Standard in Technology (NIST) 2010 and 2008 Speaker Recognition Evaluations databases. Evaluation results show that the proposed method outperforms different conventional methods in speaker age estimation.

\n Contains fulltext :\n 102327.pdf (author's version ) (Open Access)\n

In this paper, a new method called Maximum Likelihood Neural Regression (MLNR) is introduced for Rapid Speaker Adaptation (RSA). MLNR, which is conceptually simple, adapts the Gaussian means of a speaker independent (SI) model to the data of a new speaker by assuming a non-linear mapping from the SI Gaussian means to the adapted Gaussian means. It performs a non linear regression between maximum likelihood (ML) estimates of the means and the speaker independent means using General Regression Neural Networks (GRNN). Evaluation on the Wall Street Journal benchmark shows that the suggested scheme outperforms different conventional approaches.