1,426 research outputs found
An Improved Speech Emotion Classification Approach Based on Optimal Voiced Unit
Emotional speech recognition (ESR) has significant role in human-computer interaction. ESR methodology involves audio segmentation for selecting units to analyze, extract features relevant to emotion, and finally perform a classification process. Previous research assumed that a single utterance was the unit of analysis. They believed that the emotional state remained constant during the utterance, even though the emotional state could change over time, even within a single utterance. As a result, using an utterance as a single unit is ineffective for this purpose. The study’s goal is to discover a new voiced unit that can be utilized to improve ESR accuracy. Several voiced units based on voiced segments were investigated. To determine the best-voiced unit, each unit is evaluated using an ESR based on a support vector machine classifier. The proposed method was validated using three datasets: EMO-DB, EMOVO, and SAVEE. Experimental results revealed that a voiced unit with five-voiced segments has the highest recognition rate. The emotional state of the overall utterance is decided by a majority vote of its parts’ emotional states. The proposed method outperforms the traditional method in terms of classification outcomes. EMO-DB, EMOVO, and SAVEE improve their recognition rates by 12%, 27%, and 23%, respectively
Investigating spoken emotion : the interplay of language and facial expression
This thesis aims to investigate how spoken expressions of emotions are influenced by the characteristics of spoken language and the facial emotion expression. The first three chapters examined how production and perception of emotions differed between Cantonese (tone language) and English (non-tone language). The rationale for this contrast was that the acoustic property of Fundamental Frequency (F0) may be used differently in the production and perception of spoken expressions in tone languages as F0 may be preserved as a linguistic resource for the production of lexical tones. To test this idea, I first developed the Cantonese Audio-visual Emotional Speech (CAVES) database, which was then used as stimuli in all the studies presented in this thesis (Chapter 1). An emotion perception study was then conducted to examine how three groups of participants (Australian English, Malaysian Malay and Hong Kong Cantonese speakers) identified spoken expression of emotions that were produced in either English or Cantonese (Chapter 2). As one of the aims of this study was to disambiguate the effects of language from culture, these participants were selected on the basis that they either shared similarities in language type (non-tone language, Malay and English) or culture (collectivist culture, Cantonese and Malay). The results showed that a greater similarity in emotion perception was observed between those who spoke a similar type of language, as opposed to those who shared a similar culture. This suggests some intergroup differences in emotion perception may be attributable to cross-language differences. Following up on these findings, an acoustic analysis study (Chapter 3) showed that compared to English spoken expression of emotions, Cantonese expressions had less F0 related cues (median and flatter F0 contour) and also the use of F0 cues was different. Taken together, these results show that language characteristics (n F0 usage) interact with the production and perception of spoken expression of emotions. The expression of disgust was used to investigate how facial expressions of emotions affect speech articulation. The rationale for selecting disgust was that the facial expression of disgust involves changes to the mouth region such as closure and retraction of the lips, and these changes are likely to have an impact on speech articulation. To test this idea, an automatic lip segmentation and measurement algorithm was developed to quantify the configuration of the lips from images (Chapter 5). By comparing neutral to disgust expressive speech, the results showed that disgust expressive speech is produced with significantly smaller vertical mouth opening, greater horizontal mouth opening and lower first and second formant frequencies (F1 and F2). Overall, this thesis provides an insight into how aspects of expressive speech may be shaped by specific (language type) and universal (face emotion expression) factors
A Study of Accomodation of Prosodic and Temporal Features in Spoken Dialogues in View of Speech Technology Applications
Inter-speaker accommodation is a well-known property of human speech and human interaction in general. Broadly it refers to the behavioural patterns of two (or more) interactants and the effect of the (verbal and non-verbal) behaviour of each to that of the other(s). Implementation of thisbehavior in spoken dialogue systems is desirable as an improvement on the naturalness of humanmachine interaction. However, traditional qualitative descriptions of accommodation phenomena do not provide sufficient information for such an implementation. Therefore, a quantitativedescription of inter-speaker accommodation is required. This thesis proposes a methodology of monitoring accommodation during a human or humancomputer dialogue, which utilizes a moving average filter over sequential frames for each speaker. These frames are time-aligned across the speakers, hence the name Time Aligned Moving Average (TAMA). Analysis of spontaneous human dialogue recordings by means of the TAMA methodology reveals ubiquitous accommodation of prosodic features (pitch, intensity and speech rate) across interlocutors, and allows for statistical (time series) modeling of the behaviour, in a way which is meaningful for implementation in spoken dialogue system (SDS) environments.In addition, a novel dialogue representation is proposed that provides an additional point of view to that of TAMA in monitoring accommodation of temporal features (inter-speaker pause length and overlap frequency). This representation is a percentage turn distribution of individual speakercontributions in a dialogue frame which circumvents strict attribution of speaker-turns, by considering both interlocutors as synchronously active. Both TAMA and turn distribution metrics indicate that correlation of average pause length and overlap frequency between speakers can be attributed to accommodation (a debated issue), and point to possible improvements in SDS “turntaking” behaviour. Although the findings of the prosodic and temporal analyses can directly inform SDS implementations, further work is required in order to describe inter-speaker accommodation sufficiently, as well as to develop an adequate testing platform for evaluating the magnitude ofperceived improvement in human-machine interaction. Therefore, this thesis constitutes a first step towards a convincingly useful implementation of accommodation in spoken dialogue systems
Models and Analysis of Vocal Emissions for Biomedical Applications
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Children\u27s Sensitivity to Pitch Variation in Language
Children acquire consonant and vowel categories by 12 months, but take much longer to learn to interpret perceptible variation. This dissertation considers children’s interpretation of pitch variation. Pitch operates, often simultaneously, at different levels of linguistic structure. English-learning children must disregard pitch at the lexical level—since English is not a tone language—while still attending to pitch for its other functions. Chapters 1 and 5 outline the learning problem and suggest ways children might solve it. Chapter 2 demonstrates that 2.5-year-olds know pitch cannot differentiate words in English. Chapter 3 finds that not until age 4–5 do children correctly interpret pitch cues to emotions. Chapter 4 demonstrates some sensitivity between 2.5 and 5 years to the pitch cue to lexical stress, but continuing difficulties at the older ages. These findings suggest a late trajectory for interpretation of prosodic variation; throughout, I propose explanations for this protracted time-course
Are words easier to learn from infant- than adult-directed speech? A quantitative corpus-based investigation
We investigate whether infant-directed speech (IDS) could facilitate word
form learning when compared to adult-directed speech (ADS). To study this, we
examine the distribution of word forms at two levels, acoustic and
phonological, using a large database of spontaneous speech in Japanese. At the
acoustic level we show that, as has been documented before for phonemes, the
realizations of words are more variable and less discriminable in IDS than in
ADS. At the phonological level, we find an effect in the opposite direction:
the IDS lexicon contains more distinctive words (such as onomatopoeias) than
the ADS counterpart. Combining the acoustic and phonological metrics together
in a global discriminability score reveals that the bigger separation of
lexical categories in the phonological space does not compensate for the
opposite effect observed at the acoustic level. As a result, IDS word forms are
still globally less discriminable than ADS word forms, even though the effect
is numerically small. We discuss the implication of these findings for the view
that the functional role of IDS is to improve language learnability.Comment: Draf
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