Development of a Real-time Embedded System for Speech Emotion Recognition

Speech emotion recognition is one of the latest challenges in speech processing and Human Computer Interaction (HCI) in order to address the operational needs in real world applications. Besides human facial expressions, speech has proven to be one of the most promising modalities for automatic human emotion recognition. Speech is a spontaneous medium of perceiving emotions which provides in-depth information related to different cognitive states of a human being. In this context, we introduce a novel approach using a combination of prosody features (i.e. pitch, energy, Zero crossing rate), quality features (i.e. Formant Frequencies, Spectral features etc.), derived features ((i.e.) Mel-Frequency Cepstral Coefficient (MFCC), Linear Predictive Coding Coefficients (LPCC)) and dynamic feature (Mel-Energy spectrum dynamic Coefficients (MEDC)) for robust automatic recognition of speaker’s emotional states. Multilevel SVM classifier is used for identification of seven discrete emotional states namely angry, disgust, fear, happy, neutral, sad and surprise in ‘Five native Assamese Languages’. The overall experimental results using MATLAB simulation revealed that the approach using combination of features achieved an average accuracy rate of 82.26% for speaker independent cases. Real time implementation of this algorithm is prepared on ARM CORTEX M3 board

Naturalistic Emotional Speech Corpora with Large Scale Emotional Dimension Ratings

The investigation of the emotional dimensions of speech is dependent on large sets of reliable data. Existing work has been carried out on the creation of emotional speech corpora and the acoustic analysis of emotional speech and this research seeks to buildupon this work while suggesting new methods and areas of potential. A review of the literature determined that a two dimensional emotional model of activation and evaluation was the ideal method for representing the emotional states expressed inspeech. Two case studies were carried out to investigate methods of obtaining naturalunderlying emotional speech in a high quality audio environment, the results of which were used to design a final experimental procedure to elicit natural underlying emotional speech. The speech obtained in this experiment was used in the creation ofa speech corpus that was underpinned by a persistent backend database that incorporated a three-tiered annotation methodology. This methodology was used to comprehensively annotate the metadata, acoustic data and emotional data of the recorded speech. Structuring the three levels of annotation and the assets in a persistent backend database allowed interactive web-based tools to be developed; aweb-based listening tool was developed to obtain a large amount of ratings for the assets that were then written back to the database for analysis. Once a large amount of ratings had been obtained, statistical analysis was used to determine the dimensionalrating for each asset. Acoustic analysis of the underlying emotional speech was then carried out and determined that certain acoustic parameters were correlated with the activation dimension of the dimensional model. This substantiated some of thefindings in the literature review and further determined that spectral energy was strongly correlated with the activation dimension in relation to underlying emotional speech. The lack of a correlation for certain acoustic parameters in relation to the evaluation dimension was also determined, again substantiating some of the findings in the literature.The work contained in this thesis makes a number of contributions to the field: the development of an experimental design to elicit natural underlying emotional speech in a high quality audio environment; the development and implementation of acomprehensive three-tiered corpus annotation methodology; the development and implementation of large scale web based listening tests to rate the emotional dimensions of emotional speech; the determination that certain acoustic parameters are correlated with the activation dimension of a dimensional emotional model inrelation to natural underlying emotional speech and the determination that certain acoustic parameters are not correlated with the evaluation dimension of a twodimensional emotional model in relation to natural underlying emotional speech

The Czech Broadcast Conversation Corpus

This paper presents the final version of the Czech Broadcast Conversation Corpus that will shortly be released at the Linguistic Data Consortium (LDC). The corpus contains 72 recordings of a radio discussion program, which yields about 33 hours of transcribed conversational speech from 128 speakers. The release does not only include verbatim transcripts and speaker information, but also structural metadata (MDE) annotation that involves labeling of sentence-like unit boundaries, marking of non-content words like filled pauses and discourse markers, and annotation of speech disfluencies. The MDE annotation is based on the LDC's annotation standard for English, with changes applied to accommodate phenomena that are specific for Czech. In addition to its importance to speech recognition, speaker diarization, and structural metadata extraction research, the corpus is also useful for linguistic analysis of conversational Czech

Are affective speakers effective speakers? – Exploring the link between the vocal expression of positive emotions and communicative effectiveness

This thesis explores the effect of vocal affect expression on communicative effectiveness. Two studies examined whether positive speaker affect facilitates the encoding and decoding of the message, combining methods from Phonetics and Psychology.This research has been funded through a Faculty Studentship by the University of Stirling and a Fellowship by the German Academic Exchange Service (DAAD)

Detecting emotions from speech using machine learning techniques

D.Phil. (Electronic Engineering

A computational memory and processing model for prosody

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts & Sciences, 1999.Includes bibliographical references (p. 209-226).This thesis links processing in working memory to prosody in speech, and links different working memory capacities to different prosodic styles. It provides a causal account of prosodic differences and an architecture for reproducing them in synthesized speech. The implemented system mediates text-based information through a model of attention and working memory. The main simulation parameter of the memory model quantifies recall. Changing its value changes what counts as given and new information in a text, and therefore determines the intonation with which the text is uttered. Other aspects of search and storage in the memory model are mapped to the remainder of the continuous and categorical features of pitch and timing, producing prosody in three different styles: for small recall values, the exaggerated and sing-song melodies of children's speech; for mid-range values, an adult expressive style; for the largest values, the prosody of a speaker who is familiar with the text, and at times sounds bored or irritated. In addition, because the storage procedure is stochastic, the prosody from simulation to simulation varies, even for identical control parameters. As with with human speech, no two renditions are alike. Informal feedback indicates that the stylistic differences are recognizable and that the prosody is improved over current offerings. A comparison with natural data shows clear and predictable trends although not at significance. However, a comparison within the natural data also did not produce results at significance. One practical contribution of this work is a text mark-up schema consisting of relational annotations to grammatical structures. Another is the product - varied and plausible prosody in synthesized speech. The main theoretical contribution is to show that resource-bound cognitive activity has prosodic correlates, thus providing a rationale for the individual and stylistic differences in melody and rhythm that are ubiquitous in human speech.by Janet Elizabeth Cahn.Ph.D

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