A combined cepstral distance method for emotional speech recognition

Affective computing is not only the direction of reform in artificial intelligence but also exemplification of the advanced intelligent machines. Emotion is the biggest difference between human and machine. If the machine behaves with emotion, then the machine will be accepted by more people. Voice is the most natural and can be easily understood and accepted manner in daily communication. The recognition of emotional voice is an important field of artificial intelligence. However, in recognition of emotions, there often exists the phenomenon that two emotions are particularly vulnerable to confusion. This article presents a combined cepstral distance method in two-group multi-class emotion classification for emotional speech recognition. Cepstral distance combined with speech energy is well used as speech signal endpoint detection in speech recognition. In this work, the use of cepstral distance aims to measure the similarity between frames in emotional signals and in neutral signals. These features are input for directed acyclic graph support vector machine classification. Finally, a two-group classification strategy is adopted to solve confusion in multi-emotion recognition. In the experiments, Chinese mandarin emotion database is used and a large training set (1134 + 378 utterances) ensures a powerful modelling capability for predicting emotion. The experimental results show that cepstral distance increases the recognition rate of emotion sad and can balance the recognition results with eliminating the over fitting. And for the German corpus Berlin emotional speech database, the recognition rate between sad and boring, which are very difficult to distinguish, is up to 95.45%

Automated rating of patient and physician emotion in primary care visits

OBJECTIVE: Train machine learning models that automatically predict emotional valence of patient and physician in primary care visits. METHODS: Using transcripts from 353 primary care office visits with 350 patients and 84 physicians (Cook, 2002 [1], Tai-Seale et al., 2015 [2]), we developed two machine learning models (a recurrent neural network with a hierarchical structure and a logistic regression classifier) to recognize the emotional valence (positive, negative, neutral) (Posner et al., 2005 [3]) of each utterance. We examined the agreement of human-generated ratings of emotional valence with machine learning model ratings of emotion. RESULTS: The agreement of emotion ratings from the recurrent neural network model with human ratings was comparable to that of human-human inter-rater agreement. The weighted-average of the correlation coefficients for the recurrent neural network model with human raters was 0.60, and the human rater agreement was also 0.60. CONCLUSIONS: The recurrent neural network model predicted the emotional valence of patients and physicians in primary care visits with similar reliability as human raters. PRACTICE IMPLICATIONS: As the first machine learning-based evaluation of emotion recognition in primary care visit conversations, our work provides valuable baselines for future applications that might help monitor patient emotional signals, supporting physicians in empathic communication, or examining the role of emotion in patient-centered care

Emotion Recognition using Fuzzy K-Means from Oriya Speech

Communication will be intelligible when conveyed message is interpreted in right-minded. Unfortunately, the rightminded interpretation of communicated message is possible for human-human communication but it’s laborious for humanmachine communication. It is due to the inherently blending of non-verbal contents such as emotion in vocal communication which leads to difficulty in human-machine interaction. In this research paper we have performed experiment to recognize emotions like anger, sadness, astonish, fear, happiness and neutral using fuzzy K-Means algorithm from Oriya elicited speech collected from 35 Oriya speaking people aged between 22- 58 years belonging to different provinces of Orissa. We have achieved the accuracy of 65.16% in recognizing above six mentioned emotions by incorporating mean pitch, first two formants, jitter, shimmer and energy as feature vectors for this research work. Emotion recognition has many vivid applications in different domains like call centers, spoken tutoring systems, spoken dialogue research, human-robotic interfaces etc

Need of Boosted GMM in Speech Emotion Recognition System Implemented Using Gaussian Mixture Model

Speech feeling recognition is a vital issue that affects the human machine interaction. Automatic recognition of human feeling in speech aims at recognizing the underlying spirit of a speaker from the speech signal. Gaussian mixture models (GMMs) and therefore the minimum error rate classifier (i.e., theorem optimum classifier) is widespread and effective tools for speech feeling recognition. Typically, GMMs are wont to model the class-conditional distributions of acoustic options and their parameters are calculable by the expectation maximization (EM) algorithmic rule supported a coaching information set. During this paper, we have a tendency to introduce a boosting algorithmic rule for faithfully and accurately estimating the class-conditional GMMs. The ensuing algorithmic rule is known as the Boosted-GMM algorithmic rule. Our speech feeling recognition experiments show that the feeling recognition rates are effectively and considerably boosted by the Boosted-GMM algorithmic rule as compared to the EM-GMM algorithmic rule. During this interaction, human beings have some feelings that they want to convey to their communication partner with whom they are communicating, and then their communication partner may be the human or machine. This work dependent on the emotion recognition of the human beings from their speech signal. Emotion recognition from the speaker’s speech is very difficult because of the following reasons: Because of the existence of the different sentences, speakers, speaking styles, speaking rates accosting variability was introduced. The same utterance may show different emotions. Therefore, it is very difficult to differentiate these portions of utterance. Another problem is that emotion expression is depending on the speaker and his or her culture and environment. As the culture and environment gets change the speaking style also gets change, which is another challenge in front of the speech emotion recognition system.Human beings normally used their essential potentials to make communication better between themselves as well as between human and machine. During this interaction, human beings have some feelings that they want to convey to their communication partner with whom they are communicating, and then their communication partner may be the human or machine. This dissertation work dependent on the emotion recognition of the human beings from their speech signal. In this chapter introduction of the speech emotion recognition based on the problem overview and need of the system is provided. Emotional speech recognition aims at automatically identifying the emotional or physical state of a human being from his or her voice. Although feeling detection from speech could be a comparatively new field of analysis, it is several potential applications. In human-computer or human-human interaction systems, feeling recognition systems might give users with improved services by being adaptative to their emotions. The body of labor on sleuthing feeling in speech is sort of restricted. Currently, researchers area unit still debating what options influence the popularity of feeling in speech. There is conjointly appreciable uncertainty on the simplest algorithmic program for classifying feeling, and those emotions to category along.

A Review on Speech Emotion Recognition

Emotion recognition from Audio signal Recognition is a recent research topic in the Human Computer Interaction. The demand has risen for increasing communication interface between humans and digital media. Many researchers are working in order to improve their accuracy. But still there is lack of complete system which can recognize emotions from speech. In order to make the human and digital machine interaction more natural, the computer should able to recognize emotional states in the same way as human. The efficiency of emotion recognition system depends on type of features extracted and classifier used for detection of emotions. There are some fundamental emotions such as: Happy, Angry, Sad, Depressed, Bored, Anxiety, Fear and Nervous. These signals were preprocessed and analyzed using various techniques. In feature extraction various parameters used to form a feature vector are: fundamental frequency, pitch contour, formants, duration (pause length ratio) etc. These features are further classified into different emotions. This research work is the study of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system. The first one is the choice of suitable features for speech representation. The second issue is the design of an appropriate classification scheme and the third issue is the proper preparation of an emotional speech database for evaluating system performanc

Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic

If understanding sentiments is already a difficult task in human‐human communication, this becomes extremely challenging when a human‐computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network‐based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID‐19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID‐19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian‐language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SE

Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition

Speech Emotion Recognition (SER) plays a pivotal role in enhancing human-computer interaction by enabling a deeper understanding of emotional states across a wide range of applications, contributing to more empathetic and effective communication. This study proposes an innovative approach that integrates self-supervised feature extraction with supervised classification for emotion recognition from small audio segments. In the preprocessing step, to eliminate the need of crafting audio features, we employed a self-supervised feature extractor, based on the Wav2Vec model, to capture acoustic features from audio data. Then, the output featuremaps of the preprocessing step are fed to a custom designed Convolutional Neural Network (CNN)-based model to perform emotion classification. Utilizing the ShEMO dataset as our testing ground, the proposed method surpasses two baseline methods, i.e. support vector machine classifier and transfer learning of a pretrained CNN. comparing the propose method to the state-of-the-art methods in SER task indicates the superiority of the proposed method. Our findings underscore the pivotal role of deep unsupervised feature learning in elevating the landscape of SER, offering enhanced emotional comprehension in the realm of human-computer interactions

Voice Feature Extraction for Gender and Emotion Recognition

Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. This led to the development of a model for “Voice feature extraction for Emotion and Gender Recognition”. The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have different voice characteristics due to their acoustical and perceptual differences along with a variety of emotions which convey their own unique perceptions. In order to explore this area, feature extraction requires pre- processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre- processing using Voice Activity Detector (VAD), feature extraction using Mel-Frequency Cepstral Coefficient (MFCC), feature reduction by Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in the healthcare sector, virtual assistants, security purposes and other fields related to the Human Machine Interaction domain.&nbsp