Predicting Head Pose in Dyadic Conversation

Natural movement plays a significant role in realistic speech animation. Numerous studies have demonstrated the contribution visual cues make to the degree we, as human observers, find an animation acceptable. Rigid head motion is one visual mode that universally co-occurs with speech, and so it is a reasonable strategy to seek features from the speech mode to predict the head pose. Several previous authors have shown that prediction is possible, but experiments are typically confined to rigidly produced dialogue. Expressive, emotive and prosodic speech exhibit motion patterns that are far more difficult to predict with considerable variation in expected head pose. People involved in dyadic conversation adapt speech and head motion in response to the others’ speech and head motion. Using Deep Bi-Directional Long Short Term Memory (BLSTM) neural networks, we demonstrate that it is possible to predict not just the head motion of the speaker, but also the head motion of the listener from the speech signal

Prediction of Head Motion from Speech Waveforms with a Canonical-Correlation-Constrained Autoencoder

This study investigates the direct use of speech waveforms to predict head motion for speech-driven head-motion synthesis, whereas the use of spectral features such as MFCC as basic input features together with additional features such as energy and F0 is common in the literature. We show that, rather than combining different features that originate from waveforms, it is more effective to use waveforms directly predicting corresponding head motion. The challenge with the waveform-based approach is that waveforms contain a large amount of information irrelevant to predict head motion, which hinders the training of neural networks. To overcome the problem, we propose a canonical-correlation-constrained autoencoder (CCCAE), where hidden layers are trained to not only minimise the error but also maximise the canonical correlation with head motion. Compared with an MFCC-based system, the proposed system shows comparable performance in objective evaluation, and better performance in subject evaluation.Comment: head motion synthesis, speech-driven animation, deep canonically correlated autoencode

Affective social anthropomorphic intelligent system

Human conversational styles are measured by the sense of humor, personality, and tone of voice. These characteristics have become essential for conversational intelligent virtual assistants. However, most of the state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret the affective semantics of human voices. This research proposes an anthropomorphic intelligent system that can hold a proper human-like conversation with emotion and personality. A voice style transfer method is also proposed to map the attributes of a specific emotion. Initially, the frequency domain data (Mel-Spectrogram) is created by converting the temporal audio wave data, which comprises discrete patterns for audio features such as notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used to predict seven different affective states from voice. The voice is also fed parallelly to the deep-speech, an RNN model that generates the text transcription from the spectrogram. Then the transcripted text is transferred to the multi-domain conversation agent using blended skill talk, transformer-based retrieve-and-generate generation strategy, and beam-search decoding, and an appropriate textual response is generated. The system learns an invertible mapping of data to a latent space that can be manipulated and generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to voice synthesize and style transfer. Finally, the waveform is generated using WaveGlow from the spectrogram. The outcomes of the studies we conducted on individual models were auspicious. Furthermore, users who interacted with the system provided positive feedback, demonstrating the system's effectiveness.Comment: Multimedia Tools and Applications (2023

Building and Designing Expressive Speech Synthesis

We know there is something special about speech. Our voices are not just a means of communicating. They also give a deep impression of who we are and what we might know. They can betray our upbringing, our emotional state, our state of health. They can be used to persuade and convince, to calm and to excite. As speech systems enter the social domain they are required to interact, support and mediate our social relationships with 1) each other, 2) with digital information, and, increasingly, 3) with AI-based algorithms and processes. Socially Interactive Agents (SIAs) are at the fore- front of research and innovation in this area. There is an assumption that in the future “spoken language will provide a natural conversational interface between human beings and so-called intelligent systems.” [Moore 2017, p. 283]. A considerable amount of previous research work has tested this assumption with mixed results. However, as pointed out “voice interfaces have become notorious for fostering frustration and failure” [Nass and Brave 2005, p.6]. It is within this context, between our exceptional and intelligent human use of speech to communicate and interact with other humans, and our desire to leverage this means of communication for artificial systems, that the technology, often termed expressive speech synthesis uncomfortably falls. Uncomfortably, because it is often overshadowed by issues in interactivity and the underlying intelligence of the system which is something that emerges from the interaction of many of the components in a SIA. This is especially true of what we might term conversational speech, where decoupling how things are spoken, from when and to whom they are spoken, can seem an impossible task. This is an even greater challenge in evaluation and in characterising full systems which have made use of expressive speech. Furthermore when designing an interaction with a SIA, we must not only consider how SIAs should speak but how much, and whether they should even speak at all. These considerations cannot be ignored. Any speech synthesis that is used in the context of an artificial agent will have a perceived accent, a vocal style, an underlying emotion and an intonational model. Dimensions like accent and personality (cross speaker parameters) as well as vocal style, emotion and intonation during an interaction (within-speaker parameters) need to be built in the design of a synthetic voice. Even a default or neutral voice has to consider these same expressive speech synthesis components. Such design parameters have a strong influence on how effectively a system will interact, how it is perceived and its assumed ability to perform a task or function. To ignore these is to blindly accept a set of design decisions that ignores the complex effect speech has on the user’s successful interaction with a system. Thus expressive speech synthesis is a key design component in SIAs. This chapter explores the world of expressive speech synthesis, aiming to act as a starting point for those interested in the design, building and evaluation of such artificial speech. The debates and literature within this topic are vast and are fundamentally multidisciplinary in focus, covering a wide range of disciplines such as linguistics, pragmatics, psychology, speech and language technology, robotics and human-computer interaction (HCI), to name a few. It is not our aim to synthesise these areas but to give a scaffold and a starting point for the reader by exploring the critical dimensions and decisions they may need to consider when choosing to use expressive speech. To do this, the chapter explores the building of expressive synthesis, highlighting key decisions and parameters as well as emphasising future challenges in expressive speech research and development. Yet, before these are expanded upon we must first try and define what we actually mean by expressive speech

Analysis and automatic identification of spontaneous emotions in speech from human-human and human-machine communication

383 p.This research mainly focuses on improving our understanding of human-human and human-machineinteractions by analysing paricipants¿ emotional status. For this purpose, we have developed andenhanced Speech Emotion Recognition (SER) systems for both interactions in real-life scenarios,explicitly emphasising the Spanish language. In this framework, we have conducted an in-depth analysisof how humans express emotions using speech when communicating with other persons or machines inactual situations. Thus, we have analysed and studied the way in which emotional information isexpressed in a variety of true-to-life environments, which is a crucial aspect for the development of SERsystems. This study aimed to comprehensively understand the challenge we wanted to address:identifying emotional information on speech using machine learning technologies. Neural networks havebeen demonstrated to be adequate tools for identifying events in speech and language. Most of themaimed to make local comparisons between some specific aspects; thus, the experimental conditions weretailored to each particular analysis. The experiments across different articles (from P1 to P19) are hardlycomparable due to our continuous learning of dealing with the difficult task of identifying emotions inspeech. In order to make a fair comparison, additional unpublished results are presented in the Appendix.These experiments were carried out under identical and rigorous conditions. This general comparisonoffers an overview of the advantages and disadvantages of the different methodologies for the automaticrecognition of emotions in speech

Predicting Head Pose From Speech

Speech animation, the process of animating a human-like model to give the impression it is talking, most commonly relies on the work of skilled animators, or performance capture. These approaches are time consuming, expensive, and lack the ability to scale. This thesis develops algorithms for content driven speech animation; models that learn visual actions from data without semantic labelling, to predict realistic speech animation from recorded audio. We achieve these goals by _rst forming a multi-modal corpus that represents the style of speech we want to model; speech that is natural, expressive and prosodic. This allows us to train deep recurrent neural networks to predict compelling animation. We _rst develop methods to predict the rigid head pose of a speaker. Predicting the head pose of a speaker from speech is not wholly deterministic, so our methods provide a large variety of plausible head pose trajectories from a single utterance. We then apply our methods to learn how to predict the head pose of the listener while in conversation, using only the voice of the speaker. Finally, we show how to predict the lip sync, facial expression, and rigid head pose of the speaker, simultaneously, solely from speec