Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Unsupervised Stream-Weights Computation in Classification and Recognition Tasks

International audienceIn this paper, we provide theoretical results on the problem of optimal stream weight selection for the multi-stream classi- fication problem. It is shown, that in the presence of estimation or modeling errors using stream weights can decrease the total classification error. Stream weight estimates are computed for various conditions. Then we turn our attention to the problem of unsupervised stream weights computation. Based on the theoretical results we propose to use models and “anti-models” (class- specific background models) to estimate stream weights. A non-linear function of the ratio of the inter- to intra-class distance is used for stream weight estimation. The proposed unsupervised stream weight estimation algorithm is evaluated on both artificial data and on the problem of audio-visual speech classification. Finally the proposed algorithm is extended to the problem of audio- visual speech recognition. It is shown that the proposed algorithms achieve results comparable to the supervised minimum-error training approach under most testing conditions

Audio-visual speech processing system for Polish applicable to human-computer interaction

This paper describes audio-visual speech recognition system for Polish language and a set of performance tests under various acoustic conditions. We first present the overall structure of AVASR systems with three main areas: audio features extraction, visual features extraction and subsequently, audiovisual speech integration. We present MFCC features for audio stream with standard HMM modeling technique, then we describe appearance and shape based visual features. Subsequently we present two feature integration techniques, feature concatenation and model fusion. We also discuss the results of a set of experiments conducted to select best system setup for Polish, under noisy audio conditions. Experiments are simulating human-computer interaction in computer control case with voice commands in difficult audio environments. With Active Appearance Model (AAM) and multistream Hidden Markov Model (HMM) we can improve system accuracy by reducing Word Error Rate for more than 30%, comparing to audio-only speech recognition, when Signal-to-Noise Ratio goes down to 0dB

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

A Multimodal Sensor Fusion Architecture for Audio-Visual Speech Recognition

A key requirement for developing any innovative system in a computing environment is to integrate a sufficiently friendly interface with the average end user. Accurate design of such a user-centered interface, however, means more than just the ergonomics of the panels and displays. It also requires that designers precisely define what information to use and how, where, and when to use it. Recent advances in user-centered design of computing systems have suggested that multimodal integration can provide different types and levels of intelligence to the user interface. The work of this thesis aims at improving speech recognition-based interfaces by making use of the visual modality conveyed by the movements of the lips. Designing a good visual front end is a major part of this framework. For this purpose, this work derives the optical flow fields for consecutive frames of people speaking. Independent Component Analysis (ICA) is then used to derive basis flow fields. The coefficients of these basis fields comprise the visual features of interest. It is shown that using ICA on optical flow fields yields better classification results than the traditional approaches based on Principal Component Analysis (PCA). In fact, ICA can capture higher order statistics that are needed to understand the motion of the mouth. This is due to the fact that lips movement is complex in its nature, as it involves large image velocities, self occlusion (due to the appearance and disappearance of the teeth) and a lot of non-rigidity. Another issue that is of great interest to audio-visual speech recognition systems designers is the integration (fusion) of the audio and visual information into an automatic speech recognizer. For this purpose, a reliability-driven sensor fusion scheme is developed. A statistical approach is developed to account for the dynamic changes in reliability. This is done in two steps. The first step derives suitable statistical reliability measures for the individual information streams. These measures are based on the dispersion of the N-best hypotheses of the individual stream classifiers. The second step finds an optimal mapping between the reliability measures and the stream weights that maximizes the conditional likelihood. For this purpose, genetic algorithms are used. The addressed issues are challenging problems and are substantial for developing an audio-visual speech recognition framework that can maximize the information gather about the words uttered and minimize the impact of noise

On the Use of Speech and Face Information for Identity Verification

{T}his report first provides a review of important concepts in the field of information fusion, followed by a review of important milestones in audio-visual person identification and verification. {S}everal recent adaptive and non-adaptive techniques for reaching the verification decision (i.e., to accept or reject the claimant), based on speech and face information, are then evaluated in clean and noisy audio conditions on a common database; it is shown that in clean conditions most of the non-adaptive approaches provide similar performance and in noisy conditions most exhibit a severe deterioration in performance; it is also shown that current adaptive approaches are either inadequate or utilize restrictive assumptions. A new category of classifiers is then introduced, where the decision boundary is fixed but constructed to take into account how the distributions of opinions are likely to change due to noisy conditions; compared to a previously proposed adaptive approach, the proposed classifiers do not make a direct assumption about the type of noise that causes the mismatch between training and testing conditions. {T}his report is an extended and revised version of {IDIAP-RR} 02-33

Identity Verification Using Speech and Face Information

This article first provides an review of important concepts in the field of information fusion, followed by a review of important milestones in audio–visual person identification and verification. Several recent adaptive and nonadaptive techniques for reaching the verification decision (i.e., to accept or reject the claimant), based on speech and face information, are then evaluated in clean and noisy audio conditions on a common database; it is shown that in clean conditions most of the nonadaptive approaches provide similar performance and in noisy conditions most exhibit a severe deterioration in performance; it is also shown that current adaptive approaches are either inadequate or utilize restrictive assumptions. A new category of classifiers is then introduced, where the decision boundary is fixed but constructed to take into account how the distributions of opinions are likely to change due to noisy conditions; compared to a previously proposed adaptive approach, the proposed classifiers do not make a direct assumption about the type of noise that causes the mismatch between training and testing conditions