Heart rate monitoring using human speech spectral features

This paper attempts to establish a correlation between the human speech, emotions and human heart rate. The study highlights a possible contactless human heart rate measurement technique useful for monitoring of patient condition from realtime speech recordings. The distance between the average peak-to-peak distances in speech Mel-frequency cepstral coefficients are used as the speech features. The features when tested on 20 classifiers from the data collected from 30 subjects indicate a non-separable classification problem, however, the classification accuracies indicate the existence of strong correlation between the human speech, emotion and heart-rates

Un estudio piloto acerca del impacto de las emociones humanas sobre la proliferación celular y la expresión proteica a través de ondas acústicas

Comunicación presentada en el 54º Congreso Español de Acústica – TECNIACÚSTICA 2023, Cuenca, 18-20 de octubre de 2023.La comunicación emocional es un fenómeno multimodal que afecta a la postura, los gestos, las expresiones faciales y la voz humana. En este contexto, los estados afectivos modulan sistemáticamente las señales acústicas emitidas en la producción del habla mediante los músculos laríngeos vía sistema nervioso central, transformando la señal acústica en un medio de transmisión afectivo. Diversos trabajos han analizado los parámetros acústico-emocionales de la voz humana, concluyendo que la calidad de voz, la frecuencia fundamental o el tono juegan un papel primordial en la expresión emocional. Paralelamente, un creciente número de estudios acumulan evidencia de la capacidad de las ondas mecánicas para afectar a la proliferación celular, poniendo de relieve el papel de las ondas mecánicas como agente biofísico. La presente investigación se centra en analizar los efectos de señales acústico-emocionales sobre la proliferación celular de la línea 661W y la expresión proteica. Para tal fin, se ha diseñado y calibrado un sistema de radiación electroacústico en el interior de una incubadora de CO2 y establecido un método de captación de la señal acústico-emocional. Resultados preliminares apuntan a la capacidad de señales acústico-emocionales para influir sobre la proliferación celular.Emotional communication is a multimodal phenomenon involving posture, gestures, facial expressions and the human voice. In this context, affective states systematically modulate the acoustic signals emitted in speech production through the laryngeal muscles via the central nervous system, transforming the acoustic signal into a means of affective transmission. Several studies have analyzed the acoustic-emotional parameters of the human voice, concluding that voice quality, fundamental frequency or pitch play a major role in emotional expression. In parallel, a growing number of studies accumulate evidence of the ability of mechanical waves to affect cell proliferation, highlighting the role of mechanical waves as a biophysical agent. The present investigation focuses on analyzing the effects of acoustic-emotional signals on 661W cell proliferation and protein expression. For this purpose, an electroacoustic radiation system has been designed and calibrated inside a CO2 incubator and a method of acoustic-emotional signal capture has been established. Preliminary results point to the ability of acoustic-emotional signals to influence cell proliferation

A Survey of Information Technology Applications to Treat Fear of Public Speaking

Public speaking started to gain much attention when it comes to phobias, which is anxiety for new presenters. In some cases, specialists consider that avoiding the phenomenon which causes the phobia is sufficient treatment; in others, the exact opposite, being gradually exposed to the object of fear may lead to a cure. We have to start looking for other psychotherapeutic methods, innovative ones, to help people surpass their immense fears and improve their ability to give presentations. The current article presents a survey on discovering fear and anxiety when preventing and treating it and analyses their utility as tools for learning how to overcome this type of phobias, thus improving presentation ability. Using IT-based solutions for treating presented this fear, especially anxiety for new presenters. The current methods of dealing with the fear of public speaking will be reviewed, as well as Clarify the technology (tools, systems, and applications) based used for detecting and treatment. We will analyze research that studies how to detect fear and the ways to treat it, the concept behind their mechanism and the possibility of exploiting them in presentations.  therefore, the paper debates these IT instruments and applications in this field. Based on the results of the survey, we will propose an appropriate mechanism for detecting degrees and types of fear when presenting presentations and their treatment

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Speech Emotion Recognition (SER) has been widely used in many fields, such as smart home assistants commonly found in the market. Smart home assistants that could detect the user’s emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speech sets were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion

Dual-level segmentation method for feature extraction enhancement strategy in speech emotion recognition

The speech segmentation approach could be one of the significant factors contributing to a Speech Emotion Recognition (SER) system's overall performance. An utterance may contain more than one perceived emotion, the boundaries between the changes of emotion in an utterance are challenging to determine. Speech segmented through the conventional fixed window did not correspond to the signal changes, due to the random segment point, an arbitrary segmented frame is produced, the segment boundary might be within the sentence or in-between emotional changes. This study introduced an improvement of segment-based segmentation on a fixed-window Relative Time Interval (RTI) by using Signal Change (SC) segmentation approach to discover the signal boundary concerning the signal transition. A segment-based feature extraction enhancement strategy using a dual-level segmentation method was proposed: RTI-SC segmentation utilizing the conventional approach. Instead of segmenting the whole utterance at the relative time interval, this study implements peak analysis to obtain segment boundaries defined by the maximum peak value within each temporary RTI segment. In peak selection, over-segmentation might occur due to connections with the input signal, impacting the boundary selection decision. Two approaches in finding the maximum peaks were implemented, firstly; peak selection by distance allocation, and secondly; peak selection by Maximum function. The substitution of the temporary RTI segment with the segment concerning signal change was intended to capture better high-level statistical-based features within the signal transition. The signal's prosodic, spectral, and wavelet properties were integrated to structure a fine feature set based on the proposed method. 36 low-level descriptors and 12 statistical features and their derivative were extracted on each segment resulted in a fixed vector dimension. Correlation-based Feature Subset Selection (CFS) with the Best First search method was applied for dimensionality reduction before Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) was implemented for classification. The performance of the feature fusion constructed from the proposed method was evaluated through speaker-dependent and speaker-independent tests on EMO-DB and RAVDESS databases. The result indicated that the prosodic and spectral feature derived from the dual-level segmentation method offered a higher recognition rate for most speaker-independent tasks with a significant improvement of the overall accuracy of 82.2% (150 features), the highest accuracy among other segmentation approaches used in this study. The proposed method outperformed the baseline approach in a single emotion assessment in both full dimensions and an optimized set. The highest accuracy for every emotion was mostly contributed by the proposed method. Using the EMO-DB database, accuracy was enhanced, specifically, happy (67.6%), anger (89%), fear (85.5%), disgust (79.3%), while neutral and sadness emotion obtained a similar accuracy with the baseline method (91%) and (93.5%) respectively. A 100% accuracy for boredom emotion (female speaker) was observed in the speaker-dependent test, the highest single emotion classified, reported in this study

Acoustic differences in emotional speech of people with dysarthria

Communicating emotion is essential in building and maintaining relationships. We communicate our emotional state not just with the words we use, but also how we say them. Changes in the rate of speech, short-term energy and intonation all help to convey emotional states like 'angry', 'sad' and 'happy'. People with dysarthria, the most common speech disorder, have reduced articulatory and phonatory control. This can affect the intelligibility of their speech, especially when communicating with unfamiliar conversation partners. However, we know little about how people with dysarthria convey their emotional state, and whether they are having to make changes to their speech to achieve this. In this study, we investigated the ability of people with dysarthria, caused by cerebral palsy and Parkinson's disease, to communicate emotions in their speech, and we compared their speech to that of speakers with typical speech. A parallel database of emotional speech was collected. One female speaker with dysarthria due to cerebral palsy, 3 speakers with dysarthria due to Parkinson's disease (2 female and 1 male), and 21 typical speakers (9 female and 12 male) produced sentences with 'angry', 'happy', 'sad', and 'neutral' emotions. A number of acoustic features were analysed using linear multi-level modeling. The results show that people with dysarthria were able to control some aspects of the suprasegmental and prosodic features when attempting to communicate emotions. For most speakers the changes they made are consistent with the changes made by speakers with typical speech. Even when the changes might be different to that of typical speakers, acoustic analysis shows these were consistent for different emotions. The analysis shows that variation in energy and jitter (local absolute) are major indicators of emotion in the study