What do we do when we are about to utter speech? On which linguistic units do we rely? How do these units evolve from childhood to adulthood, or across time for a given language? How do we assemble these units under the influences of syntactic, phonological and prosodic rules? Do we plan the whole sequence at once? Do we plan the movements of the tongue, jaw, and lips underlying speech in the same way that we plan other movements? What tools have scientists developed to investigate these questions using observation of articulatory and acoustic signals? This book addresses these issues in eight chapters. Written by specialists in the field, these chapters provide the readers with a large overview of the literature, and illustrate the research challenges using selected examples of experimental studies.

Speech Production: Models, Phonetic Processes and Techniques brings together researchers from many different disciplines - computer science, dentistry, engineering, linguistics, phonetics, physiology, psychology - all with a special interest in how speech is produced. From the initial neural program to the end acoustic signal, it provides an overview of several dominant models in the speech production literature, as well as up-to-date accounts of persistent theoretical issues in the area. A particular focus is on the evaluation of information gleaned from instrumental investigations of the speech production process, including MRI, PET, ultra-sound, video-imaging, EMA, EPG, X-ray, computer simulation - and many others. The research presented in this volume considers questions such as: the feed-back vs. feed-forward control of speech; the acoustic/auditory vs. articulatory/somato-sensory domains of speech planning; the innateness of human speech; the possible architecture of a speech production model; and the realization of prosodic structure in speech. Leaders in speech research from around the world have contributed their most recent work to this volume.

This book explores the nature of cognitive representations and processes in speech motor control, based primarily on evidence from speech timing. It engages with the key question of whether phonological representations are spatio-temporal, as in the Articulatory Phonology approach, or symbolic (atemporal and non-quantitative); this issue has fundamental implications for the architecture of the speech production planning system, particularly with regard to the number of planning components and the type of timing mechanisms. Alice Turk and Stefanie Shattuck-Hufnagel outline a number of arguments in favour of an alternative to the Articulatory Phonology/Task Dynamics model. They demonstrate that a different framework is needed to account for evidence from speech and non-speech timing behaviour, and specifically that three separate planning components must be posited: Phonological Planning, Phonetic Planning, and Motor-Sensory Implementation. The approach proposed in the book provides a clearer and more comprehensive account of what is known about motor timing in general and speech timing in particular. It will be of interest to phoneticians and phonologists from all theoretical backgrounds as well as to speech clinicians and technologists.

Speech dynamics refer to the temporal characteristics in all stages of the human speech communication process. This speech “chain” starts with the formation of a linguistic message in a speaker's brain and ends with the arrival of the message in a listener's brain. Given the intricacy of the dynamic speech process and its fundamental importance in human communication, this monograph is intended to provide a comprehensive material on mathematical models of speech dynamics and to address the following issues: How do we make sense of the complex speech process in terms of its functional role of speech communication? How do we quantify the special role of speech timing? How do the dynamics relate to the variability of speech that has often been said to seriously hamper automatic speech recognition? How do we put the dynamic process of speech into a quantitative form to enable detailed analyses? And finally, how can we incorporate the knowledge of speech dynamics into computerized speech analysis and recognition algorithms? The answers to all these questions require building and applying computational models for the dynamic speech process. What are the compelling reasons for carrying out dynamic speech modeling? We provide the answer in two related aspects. First, scientific inquiry into the human speech code has been relentlessly pursued for several decades. As an essential carrier of human intelligence and knowledge, speech is the most natural form of human communication. Embedded in the speech code are linguistic (as well as para-linguistic) messages, which are conveyed through four levels of the speech chain. Underlying the robust encoding and transmission of the linguistic messages are the speech dynamics at all the four levels. Mathematical modeling of speech dynamics provides an effective tool in the scientific methods of studying the speech chain. Such scientific studies help understand why humans speak as they do and how humans exploit redundancy and variability by way of multitiered dynamic processes to enhance the efficiency and effectiveness of human speech communication. Second, advancement of human language technology, especially that in automatic recognition of natural-style human speech is also expected to benefit from comprehensive computational modeling of speech dynamics. The limitations of current speech recognition technology are serious and are well known. A commonly acknowledged and frequently discussed weakness of the statistical model underlying current speech recognition technology is the lack of adequate dynamic modeling schemes to provide correlation structure across the temporal speech observation sequence. Unfortunately, due to a variety of reasons, the majority of current research activities in this area favor only incremental modifications and improvements to the existing HMM-based state-of-the-art. For example, while the dynamic and correlation modeling is known to be an important topic, most of the systems nevertheless employ only an ultra-weak form of speech dynamics; e.g., differential or delta parameters. Strong-form dynamic speech modeling, which is the focus of this monograph, may serve as an ultimate solution to this problem. After the introduction chapter, the main body of this monograph consists of four chapters. They cover various aspects of theory, algorithms, and applications of dynamic speech models, and provide a comprehensive survey of the research work in this area spanning over past 20~years. This monograph is intended as advanced materials of speech and signal processing for graudate-level teaching, for professionals and engineering practioners, as well as for seasoned researchers and engineers specialized in speech processing

Abstract: This dissertation seeks to enhance understanding of speech mechanisms by employing computational modeling in two key areas: understanding how the brain builds speaker-independent representations of heard speech sounds and why apraxic speakers are unable to effectively generate speech motor programs. The first portion of the dissertation introduces the Neural Normalization Network model (NormNet) that has been developed to explain how the human brain is able to convert speaker-dependent acoustic information into speaker-independent language representations. NormNet is part of an emerging model of auditory streaming and speech categorization. Multiple strip representations and asymmetric competitive circuits are both used in the auditory streaming and speaker normalization parts of the model, thereby suggesting that these two circuits arose from similar neural designs. NormNet is able to explain and accomplish speaker normalization by generating pitch-independent representations of speech sounds while preserving information about speaker identity. The speaker-independent representations are categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be rapidly categorized into syllable and word representations and stably remembered by Adaptive Resonance Theory circuits. Model simulations use synthesized steady-state vowels from the Peterson and Barney (1952) database. The model achieves accuracy rates similar to those achieved in human listeners. The second portion of the dissertation investigates how brain lesions in patients with apraxia of speech (AOS) give rise to different behavioral characteristics. AOS is a disorder of the planning and/or programming of speech production without comprehension impairment and without weakness in the speech musculature. The DIVA model (Directions into Velocities of Articulators) and the GODIVA model (Gradient Order DIVA) provide a framework for theorizing about two possible subtypes of AOS. The first subtype is hypothesized to arise from damage to the inferior frontal sulcus region (IFS). This damage would result in fluent productions of erroneous or misplaced speech sounds. The second subtype is hypothesized to arise from damage to the frontal operculum region (FO). This damage would result in poorly articulated approximations of the desired syllables. These hypotheses are tested by investigating damage scenarios in DIVA and GODIVA. The results are compared to an apraxic patient case study.

The Handbook of Speech Production is the first reference work to provide an overview of this burgeoning area of study. Twenty-four chapters written by an international team of authors examine issues in speech planning, motor control, the physical aspects of speech production, and external factors that impact speech production. Contributions bring together behavioral, clinical, computational, developmental, and neuropsychological perspectives on speech production to create a rich and truly interdisciplinary resource Offers a novel and timely contribution to the literature and showcases a broad spectrum of research in speech production, methodological advances, and modeling Coverage of planning, motor control, articulatory coordination, the speech mechanism, and the effect of language on production processes

The idea that speech is a dynamic process is a tautology: whether from the standpoint of the talker, the listener, or the engineer, speech is an action, a sound, or a signal continuously changing in time. Yet, because phonetics and speech science are offspring of classical phonology, speech has been viewed as a sequence of discrete events-positions of the articulatory apparatus, waveform segments, and phonemes. Although this perspective has been mockingly referred to as "beads on a string", from the time of Henry Sweet's 19th century treatise almost up to our days specialists of speech science and speech technology have continued to conceptualize the speech signal as a sequence of static states interleaved with transitional elements reflecting the quasi-continuous nature of vocal production. This book, a collection of papers of which each looks at speech as a dynamic process and highlights one of its particularities, is dedicated to the memory of Ludmilla Andreevna Chistovich. At the outset, it was planned to be a Chistovich festschrift but, sadly, she passed away a few months before the book went to press. The 24 chapters of this volume testify to the enormous influence that she and her colleagues have had over the four decades since the publication of their 1965 monograph.