Spoken Word Recognition Using Hidden Markov Model

The main aim of this project is to develop isolated spoken word recognition system using Hidden Markov Model (HMM) with a good accuracy at all the possible frequency range of human voice. Here ten different words are recorded by different speakers including male and female and results are compared with different feature extraction methods. Earlier work includes recognition of seven small utterances using HMM with the use only one feature extraction method. This spoken word recognition system mainly divided into two major blocks. First includes recording data base and feature extraction of recorded signals. Here we use Mel frequency cepstral coefficients, linear cepstral coefficients and fundamental frequency as feature extraction methods. To obtain Mel frequency cepstral coefficients signal should go through the following: pre emphasis, framing, applying window function, Fast Fourier transform, filter bank and then discrete cosine transform, where as a linear frequency cepstral coefficients does not use Mel frequency. Second part describes HMM used for modeling and recognizing the spoken words. All the raining samples are clustered using K-means algorithm. Gaussian mixture containing mean, variance and weight are modeling parameters. Here Baum Welch algorithm is used for training the samples and re-estimate the parameters. Finally Viterbi algorithm recognizes best sequence that exactly matches for given sequence there is given spoken utterance to be recognized. Here all the simulations are done by the MATLAB tool and Microsoft window 7 operating system

Open-set Speaker Identification

This study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime, a need made ever more apparent with the recent expansion of criminal and terrorist organisations. The main focus is to enhance open-set speaker identification process within the speaker identification systems, which are affected by noisy audio data obtained under uncontrolled environments such as in the street, in restaurants or other places of businesses. Consequently, two investigations are initially carried out including the effects of environmental noise on the accuracy of open-set speaker recognition, which thoroughly cover relevant conditions in the considered application areas, such as variable training data length, background noise and real world noise, and the effects of short and varied duration reference data in open-set speaker recognition. The investigations led to a novel method termed “vowel boosting” to enhance the reliability in speaker identification when operating with varied duration speech data under uncontrolled conditions. Vowels naturally contain more speaker specific information. Therefore, by emphasising this natural phenomenon in speech data, it enables better identification performance. The traditional state-of-the-art GMM-UBMs and i-vectors are used to evaluate “vowel boosting”. The proposed approach boosts the impact of the vowels on the speaker scores, which improves the recognition accuracy for the specific case of open-set identification with short and varied duration of speech material

Speech Communication

Contains reports on five research projects.National Institutes of Health (Grant 5 RO1 NS04332-12)National Institutes of Health (Grant HD05168-04)U.S. Navy Office of Naval Research (Contract N00014-67-A-0204-0069)Joint Services Electronics Program (Contract DAAB07-74-C-0630)National Science Foundation (Grant SOC74-22167

Physiologically-Motivated Feature Extraction Methods for Speaker Recognition

Speaker recognition has received a great deal of attention from the speech community, and significant gains in robustness and accuracy have been obtained over the past decade. However, the features used for identification are still primarily representations of overall spectral characteristics, and thus the models are primarily phonetic in nature, differentiating speakers based on overall pronunciation patterns. This creates difficulties in terms of the amount of enrollment data and complexity of the models required to cover the phonetic space, especially in tasks such as identification where enrollment and testing data may not have similar phonetic coverage. This dissertation introduces new features based on vocal source characteristics intended to capture physiological information related to the laryngeal excitation energy of a speaker. These features, including RPCC, GLFCC and TPCC, represent the unique characteristics of speech production not represented in current state-of-the-art speaker identification systems. The proposed features are evaluated through three experimental paradigms including cross-lingual speaker identification, cross song-type avian speaker identification and mono-lingual speaker identification. The experimental results show that the proposed features provide information about speaker characteristics that is significantly different in nature from the phonetically-focused information present in traditional spectral features. The incorporation of the proposed glottal source features offers significant overall improvement to the robustness and accuracy of speaker identification tasks

The principle of least effort within the hierarchy of linguistic preferences: external evidence from English

The thesis is an investigation of the principle of least effort (Zipf 1949 [1972]). The principle is simple (all effort should be least) and universal (it governs the totality of human behavior). Since the principle is also functional, the thesis adopts a functional theory of language as its theoretical framework, i.e. Natural Linguistics. The explanatory system of Natural Linguistics posits that higher principles govern preferences, which, in turn, manifest themselves as concrete, specific processes in a given language. Therefore, the thesis’ aim is to investigate the principle of least effort on the basis of external evidence from English. The investigation falls into the three following strands: the investigation of the principle itself, the investigation of its application in articulatory effort and the investigation of its application in phonological processes. The structure of the thesis reflects the division of its broad aims. The first part of the thesis presents its theoretical background (Chapter One and Chapter Two), the second part of the thesis deals with application of least effort in articulatory effort (Chapter Three and Chapter Four), whereas the third part discusses the principle of least effort in phonological processes (Chapter Five and Chapter Six). Chapter One serves as an introduction, examining various aspects of the principle of least effort such as its history, literature, operation and motivation. It overviews various names which denote least effort, explains the origins of the principle and reviews the literature devoted to the principle of least effort in a chronological order. The chapter also discusses the nature and operation of the principle, providing numerous examples of the principle at work. It emphasizes the universal character of the principle from the linguistic field (low-level phonetic processes and language universals) and the non-linguistic ones (physics, biology, psychology and cognitive sciences), proving that the principle governs human behavior and choices. Chapter Two provides the theoretical background of the thesis in terms of its theoretical framework and discusses the terms used in the thesis’ title, i.e. hierarchy and preference. It justifies the selection of Natural Linguistics as the thesis’ theoretical framework by outlining its major assumptions and demonstrating its explanatory power. As far as the concepts of hierarchy and preference are concerned, the chapter provides their definitions and reviews their various understandings via decision theories and linguistic preference-based theories. Since the thesis investigates the principle of least effort in language and speech, Chapter Three considers the articulatory aspect of effort. It reviews the notion of easy and difficult sounds and discusses the concept of articulatory effort, overviewing its literature as well as various understandings in a chronological fashion. The chapter also presents the concept of articulatory gestures within the framework of Articulatory Phonology. The thesis’ aim is to investigate the principle of least effort on the basis of external evidence, therefore Chapters Four and Six provide evidence in terms of three experiments, text message studies (Chapter Four) and phonological processes in English (Chapter Six). Chapter Four contains evidence for the principle of least effort in articulation on the basis of experiments. It describes the experiments in terms of their predictions and methodology. In particular, it discusses the adopted measure of effort established by means of the effort parameters as well as their status. The statistical methods of the experiments are also clarified. The chapter reports on the results of the experiments, presenting them in a graphical way and discusses their relation to the tested predictions. Chapter Four establishes a hierarchy of speakers’ preferences with reference to articulatory effort (Figures 30, 31). The thesis investigates the principle of least effort in phonological processes, thus Chapter Five is devoted to the discussion of phonological processes in Natural Phonology. The chapter explains the general nature and motivation of processes as well as the development of processes in child language. It also discusses the organization of processes in terms of their typology as well as the order in which processes apply. The chapter characterizes the semantic properties of processes and overviews Luschützky’s (1997) contribution to NP with respect to processes in terms of their typology and incorporation of articulatory gestures in the concept of a process. Chapter Six investigates phonological processes. In particular, it identifies the issues of lenition/fortition definition and process typology by presenting the current approaches to process definitions and their typology. Since the chapter concludes that no coherent definition of lenition/fortition exists, it develops alternative lenition/fortition definitions. The chapter also revises the typology of phonological processes under effort management, which is an extended version of the principle of least effort. Chapter Seven concludes the thesis with a list of the concepts discussed in the thesis, enumerates the proposals made by the thesis in discussing the concepts and presents some questions for future research which have emerged in the course of investigation. The chapter also specifies the extent to which the investigation of the principle of least effort is a meaningful contribution to phonology

Stop consonant production : an articulation and acoustic study

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 126-131).by Kelly L. Poort.M.S

A comparison of features for large population speaker identification

Bibliography: leaves 95-104.Speech recognition systems all have one criterion in common; they perform better in a controlled environment using clean speech. Though performance can be excellent, even exceeding human capabilities for clean speech, systems fail when presented with speech data from more realistic environments such as telephone channels. The differences using a recognizer in clean and noisy environments are extreme, and this causes one of the major obstacles in producing commercial recognition systems to be used in normal environments. It is the lack of performance of speaker recognition systems with telephone channels that this work addresses. The human auditory system is a speech recognizer with excellent performance, especially in noisy environments. Since humans perform well at ignoring noise more than any machine, auditory-based methods are the promising approaches since they attempt to model the working of the human auditory system. These methods have been shown to outperform more conventional signal processing schemes for speech recognition, speech coding, word-recognition and phone classification tasks. Since speaker identification has received lot of attention in speech processing because of its waiting real-world applications, it is attractive to evaluate the performance using auditory models as features. Firstly, this study rums at improving the results for speaker identification. The improvements were made through the use of parameterized feature-sets together with the application of cepstral mean removal for channel equalization. The study is further extended to compare an auditory-based model, the Ensemble Interval Histogram, with mel-scale features, which was shown to perform almost error-free in clean speech. The previous studies of Elli to be more robust to noise were conducted on speaker dependent, small population, isolated words and now are extended to speaker independent, larger population, continuous speech. This study investigates whether the Elli representation is more resistant to telephone noise than mel-cepstrum as was shown in the previous studies, when now for the first time, it is applied for speaker identification task using the state-of-the-art Gaussian mixture model system

Text-independent speaker recognition

This research presents new text-independent speaker recognition system with multivariate tools such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) embedded into the recognition system after the feature extraction step. The proposed approach evaluates the performance of such a recognition system when trained and used in clean and noisy environments. Additive white Gaussian noise and convolutive noise are added. Experiments were carried out to investigate the robust ability of PCA and ICA using the designed approach. The application of ICA improved the performance of the speaker recognition model when compared to PCA. Experimental results show that use of ICA enabled extraction of higher order statistics thereby capturing speaker dependent statistical cues in a text-independent recognition system. The results show that ICA has a better de-correlation and dimension reduction property than PCA. To simulate a multi environment system, we trained our model such that every time a new speech signal was read, it was contaminated with different types of noises and stored in the database. Results also show that ICA outperforms PCA under adverse environments. This is verified by computing recognition accuracy rates obtained when the designed system was tested for different train and test SNR conditions with additive white Gaussian noise and test delay conditions with echo effect