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

Analysis of Speaker Adaptation Algorithms for HMM-based Speech Synthesis and a Constrained SMAPLR Adaptation Algorithm

In this paper we analyze the effects of several factors and configuration choices encountered during training and model construction when we want to obtain better and more stable adaptation in HMM-based speech synthesis. We then propose a new adaptation algorithm called constrained structural maximum a posteriori linear regression (CSMAPLR) whose derivation is based on the knowledge obtained in this analysis and on the results of comparing several conventional adaptation algorithms. Here we investigate six major aspects of the speaker adaptation: initial models transform functions, estimation criteria, and sensitivity of several linear regression adaptation algorithms algorithms. Analyzing the effect of the initial model, we compare speaker-dependent models, gender-independent models, and the simultaneous use of the gender-dependent models to single use of the gender-dependent models. Analyzing the effect of the transform functions, we compare the transform function for only mean vectors with that for mean vectors and covariance matrices. Analyzing the effect of the estimation criteria, we compare the ML criterion with a robust estimation criterion called structural MAP. We evaluate the sensitivity of several thresholds for the piecewise linear regression algorithms and take up methods combining MAP adaptation with the linear regression algorithms. We incorporate these adaptation algorithms into our speech synthesis system and present several subjective and objective evaluation results showing the utility and effectiveness of these algorithms in speaker adaptation for HMM-based speech synthesis

Stress recognition from speech signal

Předložená disertační práce se zabývá vývojem algoritmů pro detekci stresu z řečového signálu. Inovativnost této práce se vyznačuje dvěma typy analýzy řečového signálu, a to za použití samohláskových polygonů a analýzy hlasivkových pulsů. Obě tyto základní analýzy mohou sloužit k detekci stresu v řečovém signálu, což bylo dokázáno sérií provedených experimentů. Nejlepších výsledků bylo dosaženo pomocí tzv. Closing-To-Opening phase ratio příznaku v Top-To-Bottom kritériu v kombinaci s vhodným klasifikátorem. Detekce stresu založená na této analýze může být definována jako jazykově i fonémově nezávislá, což bylo rovněž dokázáno získanými výsledky, které dosahují v některých případech až 95% úspěšnosti. Všechny experimenty byly provedeny na vytvořené české databázi obsahující reálný stres, a některé experimenty byly také provedeny pro anglickou stresovou databázi SUSAS.Presented doctoral thesis is focused on development of algorithms for psychological stress detection in speech signal. The novelty of this thesis aims on two different analysis of the speech signal- the analysis of vowel polygons and the analysis of glottal pulses. By performed experiments, the doctoral thesis uncovers the possible usage of both fundamental analyses for psychological stress detection in speech. The analysis of glottal pulses in amplitude domain according to Top-To-Bottom criterion seems to be as the most effective with the combination of properly chosen classifier, which can be defined as language and phoneme independent way to stress recognition. All experiments were performed on developed Czech real stress database and some observations were also made on English database SUSAS. The variety of possibly effective ways of stress recognition in speech leads to approach very high recognition accuracy of their combination, or of their possible usage for detection of other speaker’s state, which has to be further tested and verified by appropriate databases.