Investigating NMF Speech Enhancement for Neural Network based Acoustic Models

In the light of the improvements that were made in the last years with neural network-based acoustic models, it is an interesting question whether these models are also suited for noise-robust recognition. This has not yet been fully explored, although first experiments confirm this question. Furthermore, preprocessing techniques that improve the robustness should be re-evaluated with these new models. In this work, we present experimental results to address these questions. Acoustic models based on Gaussian mixture models (GMMs), deep neural networks (DNNs), and long short-term memory (LSTM) recurrent neural networks (which have an improved ability to exploit context) are evaluated for their robustness after clean or multi-condition training. In addition, the influence of non-negative matrix factorization (NMF) for speech enhancement is investigated. Experiments are performed with the Aurora-4 database and the results show that DNNs perform slightly better than LSTMs and, as expected, both beat GMMs. Furthermore, speech enhancement is capable of improving the DNN result. Index Terms: robust speech recognition, long short-term memory, speech enhancemen

Redundant Hash Addressing for Large-Scale Query by Example Spoken Query Detection

State of the art query by example spoken term detection (QbE-STD) systems rely on representation of speech in terms of sequences of class-conditional posterior probabilities estimated by deep neural network (DNN). The posteriors are often used for pattern matching or dynamic time warping (DTW). Exploiting posterior probabilities as speech representation propounds diverse advantages in a classification system. One key property of the posterior representations is that they admit a highly effective hashing strategy that enables indexing the large archive in divisions for reducing the search complexity. Moreover, posterior indexing leads to a compressed representation and enables pronunciation dewarping and partial detection with no need for DTW. We exploit these characteristics of the posterior space in the context of redundant hash addressing for query-by-example spoken term detection (QbE-STD). We evaluate the QbE-STD system on AMI corpus and demonstrate that tremendous speedup and superior accuracy is achieved compared to the state-of-the-art pattern matching and DTW solutions. The system has great potential to enable massively large scale query detection

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

Feature Learning with Matrix Factorization Applied to Acoustic Scene Classification

International audienceIn this paper, we study the usefulness of various matrix factorization methods for learning features to be used for the specific Acoustic Scene Classification problem. A common way of addressing ASC has been to engineer features capable of capturing the specificities of acoustic environments. Instead, we show that better representations of the scenes can be automatically learned from time-frequency representations using matrix factorization techniques. We mainly focus on extensions including sparse, kernel-based, convolutive and a novel supervised dictionary learning variant of Principal Component Analysis and Nonnegative Matrix Factorization. An experimental evaluation is performed on two of the largest ASC datasets available in order to compare and discuss the usefulness of these methods for the task. We show that the unsupervised learning methods provide better representations of acoustic scenes than the best conventional hand-crafted features on both datasets. Furthermore, the introduction of a novel nonnegative supervised matrix factorization model and Deep Neural networks trained on spectrograms, allow us to reach further improvements

A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we utilize QKL with features in the quantum space to design kernel-based classifiers. Experimental results on challenging spoken command recognition tasks for a few low-resource languages, such as Arabic, Georgian, Chuvash, and Lithuanian, show that the proposed QKL-based hybrid approach attains good improvements over existing classical and quantum solutions.Comment: Submitted to ICASSP 202