A Review of Voice-Base Person Identification: State-of-the-Art

Automated person identification and authentication systems are useful for national security, integrity of electoral processes, prevention of cybercrimes and many access control applications. This is a critical component of information and communication technology which is central to national development. The use of biometrics systems in identification is fast replacing traditional methods such as use of names, personal identification numbers codes, password, etc., since nature bestow individuals with distinct personal imprints and signatures. Different measures have been put in place for person identification, ranging from face, to fingerprint and so on. This paper highlights the key approaches and schemes developed in the last five decades for voice-based person identification systems. Voice-base recognition system has gained interest due to its non-intrusive technique of data acquisition and its increasing method of continually studying and adapting to the person’s changes. Information on the benefits and challenges of various biometric systems are also presented in this paper. The present and prominent voice-based recognition methods are discussed. It was observed that these systems application areas have covered intelligent monitoring, surveillance, population management, election forensics, immigration and border control

Laugh machine

The Laugh Machine project aims at endowing virtual agents with the capability to laugh naturally, at the right moment and with the correct intensity, when interacting with human participants. In this report we present the technical development and evaluation of such an agent in one specific scenario: watching TV along with a participant. The agent must be able to react to both, the video and the participant’s behaviour. A full processing chain has been implemented, inte- grating components to sense the human behaviours, decide when and how to laugh and, finally, synthesize audiovisual laughter animations. The system was evaluated in its capability to enhance the affective experience of naive participants, with the help of pre and post-experiment questionnaires. Three interaction conditions have been compared: laughter-enabled or not, reacting to the participant’s behaviour or not. Preliminary results (the number of experiments is currently to small to obtain statistically significant differences) show that the interactive, laughter-enabled agent is positively perceived and is increasing the emotional dimension of the experiment

Acoustic Features of Different Types of Laughter in North Sami Conversational Speech

Peer reviewe

Synthesis of listener vocalizations : towards interactive speech synthesis

Spoken and multi-modal dialogue systems start to use listener vocalizations, such as uh-huh and mm-hm, for natural interaction. Generation of listener vocalizations is one of the major objectives of emotionally colored conversational speech synthesis. Success in this endeavor depends on the answers to three questions: Where to synthesize a listener vocalization? What meaning should be conveyed through the synthesized vocalization? And, how to realize an appropriate listener vocalization with the intended meaning? This thesis addresses the latter question. The investigation starts with proposing a three-stage approach: (i) data collection, (ii) annotation, and (iii) realization. The first stage presents a method to collect natural listener vocalizations from German and British English professional actors in a recording studio. In the second stage, we explore a methodology for annotating listener vocalizations -- meaning and behavior (form) annotation. The third stage proposes a realization strategy that uses unit selection and signal modification techniques to generate appropriate listener vocalizations upon user requests. Finally, we evaluate naturalness and appropriateness of synthesized vocalizations using perception studies. The work is implemented in the open source MARY text-to-speech framework, and it is integrated into the SEMAINE project\u27s Sensitive Artificial Listener (SAL) demonstrator.Dialogsysteme nutzen zunehmend Hörer-Vokalisierungen, wie z.B. a-ha oder mm-hm, für natürliche Interaktion. Die Generierung von Hörer-Vokalisierungen ist eines der zentralen Ziele emotional gefärbter, konversationeller Sprachsynthese. Ein Erfolg in diesem Unterfangen hängt von den Antworten auf drei Fragen ab: Wo bzw. wann sollten Vokalisierungen synthetisiert werden? Welche Bedeutung sollte in den synthetisierten Vokalisierungen vermittelt werden? Und wie können angemessene Hörer-Vokalisierungen mit der intendierten Bedeutung realisiert werden? Diese Arbeit widmet sich der letztgenannten Frage. Die Untersuchung erfolgt in drei Schritten: (i) Korpuserstellung; (ii) Annotation; und (iii) Realisierung. Der erste Schritt präsentiert eine Methode zur Sammlung natürlicher Hörer-Vokalisierungen von deutschen und britischen Profi-Schauspielern in einem Tonstudio. Im zweiten Schritt wird eine Methodologie zur Annotation von Hörer-Vokalisierungen erarbeitet, die sowohl Bedeutung als auch Verhalten (Form) umfasst. Der dritte Schritt schlägt ein Realisierungsverfahren vor, die Unit-Selection-Synthese mit Signalmodifikationstechniken kombiniert, um aus Nutzeranfragen angemessene Hörer-Vokalisierungen zu generieren. Schließlich werden Natürlichkeit und Angemessenheit synthetisierter Vokalisierungen mit Hilfe von Hörtests evaluiert. Die Methode wurde im Open-Source-Sprachsynthesesystem MARY implementiert und in den Sensitive Artificial Listener-Demonstrator im Projekt SEMAINE integriert

Efficient and Robust Methods for Audio and Video Signal Analysis

This thesis presents my research concerning audio and video signal processing and machine learning. Specifically, the topics of my research include computationally efficient classifier compounds, automatic speech recognition (ASR), music dereverberation, video cut point detection and video classification.Computational efficacy of information retrieval based on multiple measurement modalities has been considered in this thesis. Specifically, a cascade processing framework, including a training algorithm to set its parameters has been developed for combining multiple detectors or binary classifiers in computationally efficient way. The developed cascade processing framework has been applied on video information retrieval tasks of video cut point detection and video classification. The results in video classification, compared to others found in the literature, indicate that the developed framework is capable of both accurate and computationally efficient classification. The idea of cascade processing has been additionally adapted for the ASR task. A procedure for combining multiple speech state likelihood estimation methods within an ASR framework in cascaded manner has been developed. The results obtained clearly show that without impairing the transcription accuracy the computational load of ASR can be reduced using the cascaded speech state likelihood estimation process.Additionally, this thesis presents my work on noise robustness of ASR using a nonnegative matrix factorization (NMF) -based approach. Specifically, methods for transformation of sparse NMF-features into speech state likelihoods has been explored. The results reveal that learned transformations from NMF activations to speech state likelihoods provide better ASR transcription accuracy than dictionary label -based transformations. The results, compared to others in a noisy speech recognition -challenge show that NMF-based processing is an efficient strategy for noise robustness in ASR.The thesis also presents my work on audio signal enhancement, specifically, on removing the detrimental effect of reverberation from music audio. In the work, a linear prediction -based dereverberation algorithm, which has originally been developed for speech signal enhancement, was applied for music. The results obtained show that the algorithm performs well in conjunction with music signals and indicate that dynamic compression of music does not impair the dereverberation performance

The SSPNet-Mobile Corpus: from the detection of non-verbal cues to the inference of social behaviour during mobile phone conversations

Mobile phones are one of the main channels of communication in contemporary society. However, the effect of the mobile phone on both the process of and, also, the non-verbal behaviours used during conversations mediated by this technology, remain poorly understood. This thesis aims to investigate the role of the phone on the negotiation process as well as, the automatic analysis of non-verbal behavioural cues during conversations using mobile telephones, by following the Social Signal Processing approach. The work in this thesis includes the collection of a corpus of 60 mobile phone conversations involving 120 subjects, development of methods for the detection of non-verbal behavioural events (laughter, fillers, speech and silence) and the inference of characteristics influencing social interactions (personality traits and conflict handling style) from speech and movements while using the mobile telephone, as well as the analysis of several factors that influence the outcome of decision-making processes while using mobile phones (gender, age, personality, conflict handling style and caller versus receiver role). The findings show that it is possible to recognise behavioural events at levels well above chance level, by employing statistical language models, and that personality traits and conflict handling styles can be partially recognised. Among the factors analysed, participant role (caller versus receiver) was the most important in determining the outcome of negotiation processes in the case of disagreement between parties. Finally, the corpus collected for the experiments (the SSPNet-Mobile Corpus) has been used in an international benchmarking campaign and constitutes a valuable resource for future research in Social Signal Processing and more generally in the area of human-human communication