Elsa Speech Analyzer Review

[Jon Levatte]

WithElsa I have solve all research problems that I had. It was tough for me understand a scientific articles without using a dictionnary. Now, with Elsa is the opposite. Everything is clearer for me.

Elsa is a wonderful app for learning english

Because, before knowing Elsa, I have had a bad pronunciation and the worst intonation. But now, with Elsa, I speak like a native. I have a better understanding of learning languajes. I have no fear to speak with an American, a Canadian or an Australian because I feel that I become strong in English. The main features of this app that I love so much are: pronunciation, intonation, learning of new words.

Let me say to you that the two first features(pronunciation and intonation) are the key to become an english speaker. I recommend highly this app to everybody who want to speak like a native because Elsa has broken all my bad habits in learning english. Now, I speak confidently.

Pronunce a word or a sentence several times when it seems difficult for the app understand what I am saying. Because, I feel and i listen that I pronunce very well the word, but Elsa,sometime has some difficulties to understand it. You can work to make Elsa better in this aspect.

Thanks for the awesome review. We really appreciate you taking the time out to share your experience with us. We hope to upgrade our products' features soon to increase customers' experience. We look forward to working with you again in the future!

Self-learning with interactive communication was very helpful for me and this Elsa speak gives me feedback on my speech which I thought it wouldn't be possible without off-line teacher. Speech recognition looks it works for me so far even sometimes I doubt how it would work as it just points out the same pattern over and over.

As I mentiond in the pros I feel sometimes the speech recognition gives me the same feedback. However it's not easy to find a way to improve it in this app and I think I need to get advices from teachers on the pattern. I saw the app has a community to find a teacher but I don't think they would know my progress in the Elsa speaking.

Elsaspeak.com is an online platform that offers learners an AI-powered English speaking coach to enhance their pronunciation skills. Utilizing proprietary AI technology, the platform provides interactive short dialogues with instant feedback for users to improve their spoken English. Through speech analysis, strengths and areas for improvement are identified, allowing learners to focus on specific aspects. The platform also adapts by offering personalized learning plans that evolve based on individual progress and goals. Accessible through email or social accounts, Elsaspeak.com provides various resources including courses, games, and podcasts. Notably, it supports diverse English accents and dialects such as American, British, Australian, and Indian, catering to a wide range of learners.

The functionality of elsaspeak.com's AI revolves around offering a personalized English AI tutor, dedicated to enhancing users' English pronunciation and fluency. This is achieved through the utilization of a range of AI-driven features and technologies, including:

Elsaspeak.com serves as an online platform dedicated to refining users' English pronunciation through an AI-powered coaching system. The website presents various pricing plans that cater to distinct needs and usage preferences. A concise overview of Elsaspeak.com's pricing options is as follows:

Electroencephalography (EEG) using externally inserted electrodes can measure neural activity useful for a BCI and is safe, inexpensive, non-invasive, easy to use, portable, and maintains high temporal resolution [5]. Because EEG may be employed in BCI systems in a variety of fields by a user without the assistance of a technician or operator, it has become popular among end users. BCIs have made contributions in a variety of fields, including education, medicine, psychology, and military affairs [6]. They are primarily used in the field of affective computing and as a form of assistance for paralyzed individuals. Spelling systems, medical neuroergonomics, wheelchair control, virtual reality, robot control, mental workload monitoring, gaming, driver fatigue monitoring, environment management, biometrics systems, and emotion detection are among the most significant successes in EEG-based BCIs [7].

Behavior, speech, facial expressions, and physiological signals can all be used to identify human emotions [10,11,12]. The first three approaches are somewhat subjective. For example, the subjects under investigation may purposefully hide their genuine feelings, which could affect their performance. Emotion identification based on physiological signals is more reliable and objective [13].

BCIs are portable non-invasive sensor technologies that capture brain signals and use them as inputs for systems that understand the correlation between emotions and EEG changes to humanize HCIs [14]. The central nervous system generates EEG signals, which respond to emotional changes faster than other peripheral neural signals. Furthermore, it has been demonstrated that EEG signals provide essential features for emotional recognition [15].

Emotion is a complicated condition that expresses human awareness and is described as a reaction to environmental stimuli [16]. Emotions are, in general, reactions to ideas, memories, or events that occur in our environment. It is essential for making decisions and human interpersonal communication. People make decisions depending on their emotional states; therefore, bad emotions can lead to not only psychological but also physical difficulties. Unfavorable emotions can contribute to poor health while positive emotions can lead to higher living standards [17].

Historically, psychologists have used two techniques to characterize emotions: the discrete (basic) emotion model [18], and the dimensional model [19]. Dimensional models categorize emotions on dimensions or scales, and discrete emotion models comprise multiple major emotions and include two categories of emotions (positive and negative). Several theorists have conducted experiments to identify basic emotions and have offered a number of categorized models. Darwin [20] proposed an emotion theory that was later interpreted by Tomkins [21]. Tomkins claimed that discrete emotions comprise nine basic emotions: interest-excitement, surprise-startle, enjoyment-joy, distress-anguish, dissmell, fear-terror, anger-rage, contempt-disgust, and shame-humiliation. It is believed that these nine basic emotions play an important role in optimal mental health.

The most common resources for emotion elicitation are the International Affective Digitized Sound System (IADS) [36] and the International Affective Picture System (IAPS) [37]. These datasets contain standardized emotional stimuli. As a result, it is valuable in experimental studies. IAPS is made up of 1200 photographs divided into 20 groups of 60 images. Each photograph is assigned a valence and arousal value. The newest edition of IADS includes 167 digitally recorded natural sounds that are common in everyday life and are categorized for valence, dominance, and arousal. Using the Self-Assessment Manikin system [38], participants labeled the dataset. The authors of [39] state that emotions evoked by visual or aural stimuli are comparable. The results of affective labeling of multimedia, on the other hand, may not be generalizable to everyday situations or more interactive situations. As a result, more investigations involving interactive emotional stimuli in order to guarantee generalizability of BCI results are welcome. Only a few studies, to our knowledge, have employed more interactive situations to produce emotions, such as individuals playing games or using flight simulators.

The motivations for this review is to enable researchers to use machine learning methods to increase the rate of accurate and quick recognition of human emotional states from EEG-based BCI. The objective of this review is to identify different studies in the literature that use machine and deep learning approaches to classify human emotional states using EEG. Thus, the primary contributions of this study are to seek answers to the following questions:

Recordings of EEG are typically noisy and sensitive to interference from the environment. They are generally mingled with other signals (including EOG, ECG, and EMG ), interferences, artifacts, and noises.

EEG signals can be classified as spontaneous or evoked. During the signal acquisition process, various peripheral physiological signals will inevitably affect spontaneous EEG or evoked potentials. EEG signals are very nonlinear due to adaptation of human tissues or physiological regulation.

EEG signal change is unstable, susceptible to external environmental variables, and has a strong non-stationarity property. To discover and recognize features of EEG signals, several studies employ statistical analytic approaches.

The two forms of EEG waves are commonly classed as spontaneous and evoked. The nervous system produces a rhythmic potential fluctuation without any external stimuli, which is known as spontaneous EEG. Evoked potentials are measurable potential changes in the cerebral cortex as a result of external excitation/stimulation of the human sensory organs.

According to certain researchers who study functional brain connectivity based on EEG, there is a correlation between emotional states and specific areas of the brain. According to Ekman and Davidson [45], the left frontal portions of the brain are activated by enjoyment. The functional connection network was integrated with local activation by the authors in [46] to depict the activity of local parts of the brain that reacts to emotions and reflects the interactions between critical brain areas. Another study discovered that when individuals adopted fear emotions, their left frontal activity decreased [47]. Pleasurable emotions are associated with increased theta band power in the frontal midline while unpleasant emotions are associated with the opposite [48]. These studies reveal a correlation between changes in emotion and the characteristics of the corresponding EEG signals, which is more useful for researching EEG signal emotion classification. This also gives a neurophysiological foundation for detecting emotions from EEG data.

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