Environmentally robust ASR front-end for deep neural network acoustic models

This paper examines the individual and combined impacts of various front-end approaches on the performance of deep neural network (DNN) based speech recognition systems in distant talking situations, where acoustic environmental distortion degrades the recognition performance. Training of a DNN-based acoustic model consists of generation of state alignments followed by learning the network parameters. This paper first shows that the network parameters are more sensitive to the speech quality than the alignments and thus this stage requires improvement. Then, various front-end robustness approaches to addressing this problem are categorised based on functionality. The degree to which each class of approaches impacts the performance of DNN-based acoustic models is examined experimentally. Based on the results, a front-end processing pipeline is proposed for efficiently combining different classes of approaches. Using this front-end, the combined effects of different classes of approaches are further evaluated in a single distant microphone-based meeting transcription task with both speaker independent (SI) and speaker adaptive training (SAT) set-ups. By combining multiple speech enhancement results, multiple types of features, and feature transformation, the front-end shows relative performance gains of 7.24% and 9.83% in the SI and SAT scenarios, respectively, over competitive DNN-based systems using log mel-filter bank features.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.csl.2014.11.00

Convolutional Neural Network and Feature Transformation for Distant Speech Recognition

In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature transformation techniques: linear discriminat analysis (LDA) and maximum likelihood linear transformation (MLLT), on mel frequency cepstral coefficient (MFCC) before feeding them to CNN. We argue that transforming features could produce more discriminative features for CNN, and hence improve the robustness of speech recognition against reverberation. Our evaluations on Meeting Recorder Digits (MRD) subset of Aurora-5 database confirm that the use of LDA and MLLT transformations improve the robustness of speech recognition. It is better by 20% relative error reduction on compared to a standard DNN based speech recognition using the same number of hidden layers

Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation

This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech coefficients. The constraint imposed by dynamic features (i.e., the time derivatives of the speech coefficients) are used to enhance the smoothness of predicted coefficient trajectories in two ways. One is to obtain the enhanced speech coefficients with a least square estimation from the coefficients and dynamic features predicted by DNN. The other is to incorporate the constraint of dynamic features directly into the DNN training process using a sequential cost function. In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space. The transform is estimated to maximize the likelihood of the transformed coefficients given a model of clean speech coefficients. Unlike the DNN approach, no parallel corpus is used and no assumption on distortion types is made. The two approaches are evaluated on the REVERB Challenge 2014 tasks. Both speech enhancement and automatic speech recognition (ASR) results show that the DNN-based mappings significantly reduce the reverberation in speech and improve both speech quality and ASR performance. For the speech enhancement task, the proposed dynamic feature constraint help to improve cepstral distance, frequency-weighted segmental signal-to-noise ratio (SNR), and log likelihood ratio metrics while moderately degrades the speech-to-reverberation modulation energy ratio. In addition, the cross transform feature adaptation improves the ASR performance significantly for clean-condition trained acoustic models.Published versio

Environmentally robust ASR front-end for deep neural network acoustic models

This paper examines the individual and combined impacts of various front-end approaches on the performance of deep neural network (DNN) based speech recognition systems in distant talking situations, where acoustic environmental distortion degrades the recognition performance. Training of a DNN-based acoustic model consists of generation of state alignments followed by learning the network parameters. This paper first shows that the network parameters are more sensitive to the speech quality than the alignments and thus this stage requires improvement. Then, various front-end robustness approaches to addressing this problem are categorised based on functionality. The degree to which each class of approaches impacts the performance of DNN-based acoustic models is examined experimentally. Based on the results, a front-end processing pipeline is proposed for efficiently combining different classes of approaches. Using this front-end, the combined effects of different classes of approaches are further evaluated in a single distant microphone-based meeting transcription task with both speaker independent (SI) and speaker adaptive training (SAT) set-ups. By combining multiple speech enhancement results, multiple types of features, and feature transformation, the front-end shows relative performance gains of 7.24% and 9.83% in the SI and SAT scenarios, respectively, over competitive DNN-based systems using log mel-filter bank features