The case for automatic higher-level features in forensic speaker recognition

Abstract Approaches from standard automatic speaker recognition, which rely on cepstral features, suffer the problem of lack of interpretability for forensic applications. But the growing practice of using "higher-level" features in automatic systems offers promise in this regard. We provide an overview of automatic higher-level systems and discuss potential advantages, as well as issues, for their use in the forensic context

The case for automatic higher-level features in forensic speaker recognition

Abstract Approaches from standard automatic speaker recognition, which rely on cepstral features, suffer the problem of lack of interpretability for forensic applications. But the growing practice of using "higher-level" features in automatic systems offers promise in this regard. We provide an overview of automatic higher-level systems and discuss potential advantages, as well as issues, for their use in the forensic context

Gaussian Mixture Modelling of Broad Phonetic and Syllabic Events for Text-Independent Speaker Verification

This paper examines the usefulness of a multilingual broad syllable-based framework for text-independent speaker verification. Syllabic segmentation is used in order to obtain a convenient unit for constrained and more detailed model generation. Gaussian mixture models are chosen as a suitable modelling paradigm for initial testing of the framework. Promising results are presented for the NIST 2003 speaker recognition evaluation corpus. The syllable-based modelling technique is shown to outperform a state-of-the-art baseline GMM system. A simple selective reduction of the syllable set is also shown to give further improvement in performance. Overall, the syllable based framework presents itself as valid alternative to text-constrained speaker verification systems, with the advantage of being multilingual. The framework allows for future testing of alternative modelling paradigms, feature sets and qualitative analysis

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