Robust language recognition via adaptive language factor extraction

This paper presents a technique to adapt an acoustically based language classifier to the background conditions and speaker accents. This adaptation improves language classification on a broad spectrum of TV broadcasts. The core of the system consists of an iVector-based setup in which language and channel variabilities are modeled separately. The subsequent language classifier (the backend) operates on the language factors, i.e. those features in the extracted iVectors that explain the observed language variability. The proposed technique adapts the language variability model to the background conditions and to the speaker accents present in the audio. The effect of the adaptation is evaluated on a 28 hours corpus composed of documentaries and monolingual as well as multilingual broadcast news shows. Consistent improvements in the automatic identification of Flemish (Belgian Dutch), English and French are demonstrated for all broadcast types

A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition

This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules leading to a unified view on known derivations as well as to new formulations for certain approaches. The generic Bayesian perspective provided in this contribution thus highlights structural differences and similarities between the analyzed approaches

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

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