Sparse Subspace Modeling for Query by Example Spoken Term Detection

This paper focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. Current state-of-the-art approaches to tackle this problem rely on dynamic programming based template matching techniques using phone posterior features extracted at the output of a deep neural network (DNN). Previously, it has been shown that the space of phone posteriors is highly structured, as a union of low-dimensional subspaces. To exploit the temporal and sparse structure of the speech data, we investigate here three different QbE-STD systems based on sparse model recovery. More specifically, we use query examples to model the query subspace using dictionary for sparse coding. Reconstruction errors calculated using sparse representation of feature vectors are then used to characterize the underlying subspaces. The first approach uses these reconstruction errors in a dynamic programming framework to detect the spoken query, resulting in a much faster search compared to standard template matching. The other two methods aim at merging template matching and sparsity based approaches to further improve the performance. The first one proposes to regularize the template matching local distances using sparse reconstruction errors. The second approach aims at using the sparse reconstruction errors to rescore (improve) the template matching likelihood. Experiments on two different databases (AMI and MediaEval) show that the proposed hybrid systems perform better than a highly competitive QbE-STD baseline system.

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

Afsaneh Asaei, Hervé Bourlard, Pranay Dighe

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 representation of the test posteriors using this dictionary enables projection to the space of training data. Relying on the fact that the intrinsic di- mensions of the posterior subspaces are indeed very small and the matrix of all posteriors belonging to a class has a very low rank, we demonstrate how low-dimensional structures enable further en- hancement of the posteriors and rectify the spurious errors due to mismatch conditions. The enhanced acoustic modeling method leads to improvements in continuous speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in both clean and noisy conditions, where upto 15.4% relative reduction in word error rate (WER) is achieved.

IEEE2016

Sparse Subspace Modeling for Query by Example Spoken Term Detection

Afsaneh Asaei, Hervé Bourlard, Dhananjay Ram

We cast the problem of query by example spoken term detection (QbE-STD) as subspace detection where query and background are modeled as a union of low-dimensional subspaces. The speech exemplars used for subspace modeling consist of class-conditional posterior probabilities obtained from deep neural network (DNN). The query and background training exemplars are exploited to model the underlying low-dimensional subspaces through dictionary learning and sparse coding. Given the dictionaries characterizing the query and background speech, QbE-STD amounts to subspace detection via sparse representation and the reconstruction error is used for binary classification. Furthermore, we rigorously investigate the relationship between the proposed method and the generalized likelihood ratio test. The experimental evaluation demonstrate that the proposed method is able to detect the query given a single exemplar and performs significantly better than one of the best QbE-STD baseline systems based on template matching.

Idiap2016

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

Afsaneh Asaei, Hervé Bourlard, Pranay Dighe

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 typically very high-dimensional, recent studies have shown that the true class information is actually embedded in low-dimensional subspaces. Thus, a matrix of all posteriors belonging to a particular senone class is expected to have a very low rank. In this paper, we exploit Principal Component Analysis and Compressive Sensing based dictionary learning for low-rank and sparse modeling of senone subspaces respectively. Our hypothesis is that the principal components of DNN posterior space (termed as eigen-posteriors in this work) and Compressive Sensing dictionaries can act as suitable models to extract the well-structured low dimensional latent information and discard the undesirable high-dimensional unstructured noise present in the posteriors. Enhanced DNN posteriors thus obtained are used as soft targets for training better acoustic models to improve ASR. In this context, our approach also enables improving distant speech recognition by mapping far-field acoustic features to low-dimensional senone subspaces learned from near-field features. Experiments are performed on AMI Meeting corpus in both close-talk (IHM) and far-field (SDM) microphone settings where acoustic models trained using enhanced DNN posteriors outperform the conventional hard target based hybrid DNN-HMM systems. An information theoretic analysis is also presented to show how low-rank and sparse enhancement modify the DNN posterior space to better match the assumptions of hidden Markov model (HMM) backend.

ELSEVIER SCIENCE BV2019