Publications de Afsaneh Asaei | EPFL Graph Search

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The speech signal conveys information on different time scales from short (20--40 ms) time scale or segmental, associated to phonological and phonetic information to long (150--250 ms) time scale or supra segmental, associated to syllabic and prosodic info ...

This paper proposes a joint verification-localization structure based on split-band analysis of speech signal and the mixed voicing level. To address the problems in reverberant acoustic environments, a new fundamental frequency estimation algorithm is pro ...

A novel localization approach is proposed in order to find the position of an individual source using recordings of a single microphone in a reverberant enclosure. The multipath propagation is modeled by multiple virtual microphones as images of the actual ...

We propose a sparse coding approach to address the problem of source-sensor localization and speech reconstruction. This approach relies on designing a dictionary of spatialized signals by projecting the microphone array recordings into the array manifolds ...

We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional e ...

In this paper, a compressive sensing (CS) perspective to exemplar-based speech processing is proposed. Relying on an analytical relationship between CS formulation and statistical speech recognition (Hidden Markov Models HMM), the automatic speech recognit ...

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acou ...

Phonological features extracted by neural network have shown interesting potential for low bit rate speech vocoding. The span of phonological features is wider than the span of phonetic features, and thus fewer frames need to be transmitted. Moreover, the ...

Towards the goal of improving acoustic modeling for automatic speech recognition (ASR), this work investigates the modeling of senone subspaces in deep neural network (DNN) posteriors using low-rank and sparse modeling approaches. While DNN posteriors are ...

The present invention discloses a method, apparatus and computer program product for determining the location of a plurality of speech sources in an area of interest, comprising performing an algorithm on a signal issued by either one of said plurality of ...

We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low- dimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse represen ...

We address the problem of microphone location cali- bration where the sensor positions have a sparse spatial approximation on a discretized grid. We characterize the microphone signals as a sparse vector represented over a codebook of multi-channel signals ...

This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between the microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing ...

We cast the query by example spoken term detection (QbE-STD) problem as subspace detection where query and background subspaces are modeled as union of low-dimensional subspaces. The speech exemplars used for subspace modeling are class-conditional posteri ...

Phonological classes define articulatory-free and articulatory-bound phone attributes. Deep neural network is used to estimate the probability of phonological classes from the speech signal. In theory, a unique combination of phone attributes form a phonem ...

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

In this paper, the problem of multiple speaker localization via speech separation based on model-based sparse recovery is studies. We compare and contrast computational sparse optimization methods incorporating harmonicity and block structures as well as a ...

Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond ...

We cast the under-determined convolutive speech separation as sparse approximation of the spatial spectra of the mixing sources. In this framework we compare and contrast the major practical algorithms for structured sparse recovery of speech signal. Speci ...

We propose to use non-negative matrix factorization (NMF) to estimate the unknown pairwise distances and reconstruct a distance matrix for microphone array position calibration. We develop new multiplicative update rules for NMF with incomplete input matri ...

Afsaneh Asaei

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On Structured Sparsity of Phonological Posteriors for Linguistic Parsing

Verified Speaker Localization Utilizing Voicing Level in Split-bands

Spatial Sound Localization via Multipath Euclidean Distance Matrix Recovery

Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

Sparse Modeling of Neural Network Posterior Probabilities for Exemplar-based Speech Recognition

Structured Sparsity Models for Reverberant Speech Separation

On Compressibility of Neural Network phonological Features for Low Bit Rate Speech Coding

Low-rank and sparse subspace modeling of speech for DNN based acoustic modeling

Method, apparatus and computer program product for determining the location of a plurality of speech sources

Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition

Structured Sparse Coding for Microphone Array Location Calibration

Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees

Subspace Detection of DNN Posterior Probabilities via Sparse Representation for Query by Example Spoken Term Detection

Perceptual Information Loss due to Impaired Speech Production

Manifold Sparse Beamforming

Model-based Sparse Component Analysis for Reverberant Speech Localization

Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models

Computational Methods For Structured Sparse Component Analysis of Convolutive Speech Mixtures

On Application Of Non-Negative Matrix Factorization for Ad Hoc Microphone Array Calibration from Incomplete Noisy Distances