Emotion conveys the psychological state of a person. It is expressed by a variety of physiological changes, such as changes in blood pressure, heart beat rate, degree of sweating, and can be manifested in shaking, changes in skin coloration, facial expression, and the acoustics of speech. This research focuses on the recognition of emotion conveyed in speech. There were three main objectives of this study. One was to examine the role played by the glottal source signal in the expression of emotional speech. The second was to investigate whether it can provide improved robustness in real-world situations and in noisy environments. This was achieved through testing in clear and various noisy conditions. Finally, the performance of glottal features was compared to diverse existing and newly introduced emotional feature domains. A novel glottal symmetry feature is proposed and automatically extracted from speech. The effectiveness of several inverse filtering methods in extracting the glottal signal from speech has been examined. Other than the glottal symmetry, two additional feature classes were tested for emotion recognition domains. They are the: Tonal and Break Indices (ToBI) of American English intonation, and Mel Frequency Cepstral Coefficients (MFCC) of the glottal signal. Three corpora were specifically designed for the task. The first two investigated the four emotions: Happy, Angry, Sad, and Neutral, and the third added Fear and Surprise in a six emotions recognition task. This work shows that the glottal signal carries valuable emotional information and using it for emotion recognition has many advantages over other conventional methods. For clean speech, in a four emotion recognition task using classical prosodic features achieved 89.67% recognition, ToBI combined with classical features, reached 84.75% recognition, while using glottal symmetry alone achieved 98.74%. For a six emotions task these three methods achieved 79.62%, 90.39% and 85.37% recognition rates, respectively. Using the glottal signal also provided greater classifier robustness under noisy conditions and distortion caused by low pass filtering. Specifically, for additive white Gaussian noise at SNR = 10 dB in the six emotion task the classical features and the classical with ToBI both failed to provide successful results; speech MFCC's achieved a recognition rate of 41.43% and glottal symmetry reached 59.29%. This work has shown that the glottal signal, and the glottal symmetry in particular, provides high class separation for both the four and six emotion cases. It is confidently surpassing the performance of all other features included in this investigation in noisy speech conditions and in most clean signal conditions.

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Thesis (M.Sc.)--Flinders University, School of Medicine, Dept. of Speech Pathology and Audiology. / Typescript bound. Includes bibliographical references: (leaves 237-249) Also available electronically.

Thesis (M.Communication Pathology) -- University pf Pretoria, 2000. / Summary in English and Afrikaans.

There is significant variability in the ability of listeners to perceive degraded speech. Existing research has suggested that vocabulary knowledge is one factor that differentiates better listeners from poorer ones, though the reason for such a relationship is unclear. This study aimed to investigate whether a relationship exists between vocabulary knowledge and the type of lexical segmentation strategy listeners use in adverse conditions. This study conducted error pattern analysis using an existing dataset of 34 normal-hearing listeners (11 males, 23 females, aged 18 to 35) who participated in a speech recognition in noise task. Listeners were divided into a higher vocabulary (HV) and a lower vocabulary (LV) group based on their receptive vocabulary score on the Peabody Picture Vocabulary Test (PPVT). Lexical boundary errors (LBEs) were analysed to examine whether the groups showed differential use of syllabic strength cues for lexical segmentation. Word substitution errors (WSEs) were also analysed to examine patterns in phoneme identification. The type and number of errors were compared between the HV and LV groups. Simple linear regression showed a significant relationship between vocabulary and performance on the speech recognition task. Independent samples t-tests showed no significant differences between the HV and LV groups in Metrical Segmentation Strategy (MSS) ratio or number of LBEs. Further independent samples t-tests showed no significant differences between the WSEs produced by HV and LV groups in the degree of phonemic resemblance to the target. There was no significant difference in the proportion of target phrases to which HV and LV listeners responded. The results of this study suggest that vocabulary knowledge does not affect lexical segmentation strategy in adverse conditions. Further research is required to investigate why higher vocabulary listeners appear to perform better on speech recognition tasks.

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Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references (leaves 20-21).

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.