DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Audio-Visual Automatic Speech Recognition Using PZM, MFCC and Statistical Analysis

Audio-Visual Automatic Speech Recognition (AV-ASR) has become the most promising research area when the audio signal gets corrupted by noise. The main objective of this paper is to select the important and discriminative audio and visual speech features to recognize audio-visual speech. This paper proposes Pseudo Zernike Moment (PZM) and feature selection method for audio-visual speech recognition. Visual information is captured from the lip contour and computes the moments for lip reading. We have extracted 19th order of Mel Frequency Cepstral Coefficients (MFCC) as speech features from audio. Since all the 19 speech features are not equally important, therefore, feature selection algorithms are used to select the most efficient features. The various statistical algorithm such as Analysis of Variance (ANOVA), Kruskal-wallis, and Friedman test are employed to analyze the significance of features along with Incremental Feature Selection (IFS) technique. Statistical analysis is used to analyze the statistical significance of the speech features and after that IFS is used to select the speech feature subset. Furthermore, multiclass Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naive Bayes (NB) machine learning techniques are used to recognize the speech for both the audio and visual modalities. Based on the recognition rate combined decision is taken from the two individual recognition systems. This paper compares the result achieved by the proposed model and the existing model for both audio and visual speech recognition. Zernike Moment (ZM) is compared with PZM and shows that our proposed model using PZM extracts better discriminative features for visual speech recognition. This study also proves that audio feature selection using statistical analysis outperforms methods without any feature selection technique

Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation

This work presents a broad study on the adaptation of neural network acoustic models by means of learning hidden unit contributions (LHUC) -- a method that linearly re-combines hidden units in a speaker- or environment-dependent manner using small amounts of unsupervised adaptation data. We also extend LHUC to a speaker adaptive training (SAT) framework that leads to a more adaptable DNN acoustic model, working both in a speaker-dependent and a speaker-independent manner, without the requirements to maintain auxiliary speaker-dependent feature extractors or to introduce significant speaker-dependent changes to the DNN structure. Through a series of experiments on four different speech recognition benchmarks (TED talks, Switchboard, AMI meetings, and Aurora4) comprising 270 test speakers, we show that LHUC in both its test-only and SAT variants results in consistent word error rate reductions ranging from 5% to 23% relative depending on the task and the degree of mismatch between training and test data. In addition, we have investigated the effect of the amount of adaptation data per speaker, the quality of unsupervised adaptation targets, the complementarity to other adaptation techniques, one-shot adaptation, and an extension to adapting DNNs trained in a sequence discriminative manner.Comment: 14 pages, 9 Tables, 11 Figues in IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 24, Num. 8, 201

Hypothesis-based image segmentation for object learning and recognition

Denecke A. Hypothesis-based image segmentation for object learning and recognition. Bielefeld: Universität Bielefeld; 2010.This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition as well as for the online and interactive acquisition of visual representations. First the problem of image segmentation in general terms and next its importance for object learning in current state-of-the-art systems is introduced. Secondly a method using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterizes the novelty of the approach compared to state-of-the-art methods. Finally our technique is extended towards online adaption of model complexity and the integration of several segmentation cues. This yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition

Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end a combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 201

Spatial features of reverberant speech: estimation and application to recognition and diarization

Distant talking scenarios, such as hands-free calling or teleconference meetings, are essential for natural and comfortable human-machine interaction and they are being increasingly used in multiple contexts. The acquired speech signal in such scenarios is reverberant and affected by additive noise. This signal distortion degrades the performance of speech recognition and diarization systems creating troublesome human-machine interactions.This thesis proposes a method to non-intrusively estimate room acoustic parameters, paying special attention to a room acoustic parameter highly correlated with speech recognition degradation: clarity index. In addition, a method to provide information regarding the estimation accuracy is proposed. An analysis of the phoneme recognition performance for multiple reverberant environments is presented, from which a confusability metric for each phoneme is derived. This confusability metric is then employed to improve reverberant speech recognition performance. Additionally, room acoustic parameters can as well be used in speech recognition to provide robustness against reverberation. A method to exploit clarity index estimates in order to perform reverberant speech recognition is introduced. Finally, room acoustic parameters can also be used to diarize reverberant speech. A room acoustic parameter is proposed to be used as an additional source of information for single-channel diarization purposes in reverberant environments. In multi-channel environments, the time delay of arrival is a feature commonly used to diarize the input speech, however the computation of this feature is affected by reverberation. A method is presented to model the time delay of arrival in a robust manner so that speaker diarization is more accurately performed.Open Acces