Emotional speech is a separate channel of communication that carries the paralinguistic aspects of spoken language. Affective computing [1] is the art of recognizing emotions from various modalities. Different persons have different emotions and altogether a different way to express it. Such a system can find use in a wide variety of application areas like interactive voice based-assistant or caller-agent conversation analysis. A Study of Speech Emotion Recognition and Its Application to Mobile Services Won-Joong Yoon, Youn-Ho Cho & Kyu-Sik Park Conference paper 1395 Accesses 11 Citations Part of the Lecture Notes in Computer Science book series (LNISA,volume 4611) Abstract In this paper, a speech emotion recognition agent for mobile communication service is proposed. In particular, emotion recognition in human speech is important, as it is the primary communication tool of humans. In this way, robot can recognize emotional information as . Solution Overview Developped a speech emotion recognition platform to analyze the emotions of speakers. We analyze facial, vocal and textual emotions, using mostly deep learning based approaches. Speech Recognition Applications Applications of simple speech recognition are widespread - YouTube auto-generated subtitles, live speech transcripts, transcripts for online courses, and intelligent voice-assisted chatbots like Alexa and Siri. Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. The interface of the real-time speech recognition system. We propose a speech-emotion recognition (SER) model with an "attention-long Long Short-Term Memory (LSTM)-attention" component to combine IS09, a commonly used feature for SER, and mel spectrogram, and we analyze the reliability problem of the interactive emotional dyadic motion capture (IEMOCAP) database. This way, it will play kinds of music depending on my mood. Compare the . While humans can efficiently perform this task as a natural part of speech communication, the ability to conduct it automatically using programmable devices is still an ongoing subject of research. Emotions are an intrinsic part of speech which determines the actual state of mind of an individual. The Attention Mechanism is widely used to improve the performance of SER. Emotion Detection from Speech 1. Voice search This is, arguably, the most common use of speech recognition. 301 papers with code 5 benchmarks 36 datasets. But what can you do after you've figured out the emotions? We developped a multimodal emotion recognition platform to analyze the emotions of job candidates, in partnership with the French Employment Agency. It also includes electroencephalography ( EEG) recordings of people exposed to various stimuli ( SSVEP, resting with eyes closed, resting with eyes open, cognitive tasks) for the task of EEG-based biometrics. Currently, one of the most prominent applications of speech recognition in the internet of things is in cars. Real-Time Multimodal Emotion Recognition In a nutshell. Notebook. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Majority of the automatic speech recognition systems (ASR) are trained with neutral speech and the performance of these systems are affected due to the presence of emotional content in the speech. Why emotion recognition in speech is a significant and applicable research topic is discussed, and a system for emotion recognition using one-class- in-one neural networks is presented that achieves a recognition rate of approximately 50% when testing eight emotions. Emotion recognition in speech is a topic on which little research has been done to-date. RAVDESS Emotional speech audio, Toronto emotional speech set (TESS), CREMA-D +1. Data. Our customers want to know how people respond to ads, products, packaging and store design," he said. It is a field with growing interest and potential applications in Human-Computer Interaction, content management, social interaction, and as an add-on module in Speech Recognition and Speech-To-Text systems. Emotions play an important role in social, interaction, human intelligence, perception etc. Below, we provide an overview of the existing work on emotion recognition and related applications that make use of articulatory kinematics. In this paper, we discuss why emotion . The first database consists of 700 emotional utterances in English pronounced by 30 subjects portraying five emotional states: unemotional (normal), anger, happiness, sadness, and [] Speech emotion recognition (SER) plays an important role in real-time applications of human-machine interaction. Voice recognition and speech activation is being developed for a whole myriad of reasons. Cell link copied. Predominantly, speech emotion recognition systems are built to classify speech utterances, which comprise of one dialog turn and . Automatic Speech Recognition (ASR), Speech Recognition, or Speech-to-Text, is the program's ability to recognize human speech and process it into a written format. [31] Applications [ edit] Emotion recognition is used in society for a variety of reasons. It can also be used to monitor the psycho physiological state of a person in lie detectors. The research of deep learning-based speech emotion recognition algorithm can firstly extract more effective speech emotion features by using the advantage of machine learning in feature extraction and improving the SER system's accuracy in recognizing emotions [ 2 ]. The system is usually based on the signal of speech and emotion recognition methods. Human machine interaction is widely used nowadays in many applications. Speech emotion recognition (SER) is a challenging issue because it is not clear which features are effective for classification. Google Scholar Cross . The attention mechanism of the model focuses on emotion-related elements of the IS09 . Emotion recognition from speech: a review . Multimodal Speech Emotion Recognition Using Audio and Text. Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning. Speech Emotion Recognition App (written by Tapaswi) Introduction: (Tapaswi) Detecting emotions is one of the most important marketing strategies in today's world. Recognizing human emotion has always been a fascinating task for data scientists. Back-end recognition It involves consumers' speaking into a handheld gadget, which performs recording. Similarly, in medical sciences, virtual assessment of the patients' health is possible by listening to his/her voice. 28, 2, 483--500. The "neuro"-naissance or renaissance of neural networks has not stopped at revolutionizing automatic speech recognition. Introduction Although emotion detection from speech is a relatively new field of research, it has many potential applications. Various research is still ongoing to explore the usage of speech instead of transcribed texts for emotion recognition applications. To achieve higher this, a Convolutional Neural Network (CNN) model is used. Since a few decades, speech processing has registered considerable progress in different applications, such as speech recognition, synthesis and enhancement, source separation, etc. Common applications 1. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Facial expression recognition (FER) is widely used in several applications, including customer satisfaction recognition, human-computer interaction, medical diagnostics (disease), elderly. 1. It includes importance of prosody for speech processing applications; builds on why prosody needs to be incorporated in speech processing applications; and presents methods for extraction and representation of prosody for applications such as speaker recognition, language recognition and speech recognition. Fig. Source: Using Deep Autoencoders for Facial . In recent time, speech emotion recognition also find its applications in medicine and forensics. Various applications. david-yoon/multimodal-speech-emotion 10 Oct 2018. One of the medium of interaction is speech. Voice recognition is an AI-enabled capability that enables a software algorithm to match the identity of a customer to their voice. Facial expression recognition (FER) systems uses computer based algorithms for the instantaneous detection of facial expressions. The recognition of these emotions in human speech is considered to be the crucial aspect of human-machine interaction. Speech Emotion Recognition. Lower operational cost. The system accepts three types of speech data source, i.e., real-time recording from a microphone, a pre-recorded audio file, and a dataset consisting of multiple audio files. Speech emotion recognition is an important part of human–computer interaction, and the use of computers to analyze emotions and extract speech emotion features that can achieve high recognition rates is an important step. Emotion Recognition is an important area of research to enable effective human-computer interaction. It is also known as speech recognition (SR) in real-time mode: it embraces software that works live and enables users to speak into the screen that will record and convert the speech into readable format. 1996; Koolagudi et al. The following are among of such set-ups when speech features can aid as a means of recognizing individual emotions: i. Every year, we see . 2009). That kind of data may play an important role as semantic analysis features of . As the result of decades of research, this AI-powered digital assistant brought a touch of humanity to the sterile world of speech recognition. A subset of speech recognition is voice recognition. Emotion can be detected from different physiological signal also. Logs. Emotion recognition software has many use cases across product, marketing, sentiment analysis, visual detection, and more. By 2022, it is reported that in the US alone, 135.6M users will use a digital assistant at least once a month. They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Also, application of emotion recognition can be found in education, where applications can measure real-time learner responses to and engagement with educational content. This thesis aims to perform categorized recognition of 5 speech emotions as represented by joy, grief, anger, fear and surprise by means of algorithm with the combination of HMM and SOFMNN models so as to apply speech emotion recognition methods presented by integrated HMM/SOFMNN model to the platform of intelligent household robot. But imagine if we add the emotion recognition power into that command. Speech Emotion Recognition, abbreviated as SER, is the act of attempting to recognize human emotion and affective states from speech. Speech is the most natural way of expressing ourselves as humans. The system of emotion recognition. Developing emotion recognition systems that are based on speech has practical application benefits. 1 Relationship between angry (left panel) and sad (right panel) emotions, acoustic characteristics, and articulatory information for an utterance "compare me to" by a male speaker. It is particularly useful for enhancing naturalness in speech based human machine interaction (Schuller et al. 2. Computer Speech Lang. This way the content of a lecture can be adapted appropriately and the application also serves as mean of measuring the effectiveness of the lecturer. In this study we attempt to detect underlying emotions in recorded speech by analysing the acoustic features of the audio data of recordings. Emotion recognition system may be used in an on- We analye vocal emotions, using mostly deep learning based approaches. Since the first publications on deep learning for speech emotion recognition (in Wllmer et al., 42 a long-short term memory recurrent neural network (LSTM RNN) is used, and in Stuhlsatz et al. An applied project on " Speech Emotion Recognition submitted by Tapaswi Baskota to extrudesign.com. Second, a model with lower complexity means lower latency in recognizing emotions. Many music apps are already giving categories with different desires, so why not play that mix with just a simple "play music" command. When playing music and fluctuating the ambient room's lights as per the tendency of the dialogue. Document-Term Matrix or DTM is the usual data structure used for texts. "Based on our current customer and partners engagements, one of the most promising use cases is marketing/advertising. Emotionally related features are always extracted from speech signals for emotional classification. Recognizing Human Emotion from Audio Recording. However, the applicable rules of attention mechanism are not deeply discussed. Emotion Recognition. The main challenges in human machine interaction is detection of emotion from speech. By using this system we will be able to predict emotions such as sad, angry, surprised, calm, fearful, neutral, regret, and many more using some audio files. Speech is a composite signal containing information about individual emotions, language, tone, pitch and message to be conveyed. Majority of the automatic speech recognition systems (ASR) are trained with neutral speech and the performance of these systems are affected due to the presence of emotional content in the speech. Personal security includes gaining access to personal information and most popularly personal mobile devices. Emotions are elementary for humans, impacting perception and everyday activities like communication, learning and decision-making. In this paper 7 emotions are recognized using pitch and prosody features. Usage in education and daily life The system automatically identifies human beings' emotions from their voice. The fundamentals of a speech emotion recognition system are examined and various pre-processing, feature extraction, and classification techniques for the system are explored in this paper in order to build systems that behave intelligently like humans. Speech emotion recognition has found increasing applications in practice, e.g., in security, medicine, entertainment, education. Owing to achievements in computer vision, the emotion-sensing technology field is progressing on understanding human emotions, speech recognition, deep learning and related technologies. This project is done by Computer Science students Tapaswi, Swastika and Dhiraj. "Speech Emotion Recognition AI involves so many applications, e.g., a call center system can assess the mood of customers who call for service if they are angry or irritable, and record. 26 The advantages of this could change the way we drive and interact with our vehicles, with the overall aim of limiting driver distractions. While the former focuses on translating verbal speech into a written form, the latter identifies an individual speaker's voice. Speech emotion recognition technology has greatly facilitated medical, educational, service, automotive, and other industries. The combined spectral and differenced prosody features are considered for the . Speech emotion Recognition (SER) systems aim to facilitate the natural interaction with machines by direct voice interaction rather than exploitation ancient devices as input to know verbal content and build it straightforward for human listeners to react. The importance of emotion recognition is getting popular with improving user experience and the engagement of Voice User Interfaces (VUIs). Speech is a complex communication form that conveys information at several levels in addition to verbal content. It is an algorithm to recognize hidden feelings through tone and pitch. In human-computer or human-human interaction systems, emotion recognition systems could provide users with improved services by being adaptive to their emotions. Emotion can play an important role in decision making. There are several applications of emotional understanding such as E-learning where the tutor can change the presentation style when a learner is feeling uninterested, angry, or interested. License. Speech emotion recognition has several applications in day-to-day life. Here are some interesting examples of the applications of emotion-sensing technology. According to Winkler, emotion recognition has applications in different sectors, for example retail. Two proprietary databases of emotional utterances were used in this research. However, the research work on speech emotion recognition is mainly conducted on pre-processed databases that, in general, consist of isolated utterances or phrases. Highly Influenced View 3 excerpts, cites background Majority of the speech features used in this work are in time domain. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This Notebook has been released under the Apache 2.0 open source license. Text. In this talk I will overview my research on emotion expression and emotion recognition in speech signal and its applications. Affective information knowledge can be crucial for contextual speech recognition, which can also provide elements from the personality and psychological state of the speaker enriching the communication. Such a system can find use in a wide variety of application . I enjoy working on Speech Recognition related projects like this one. Current Applications of Facial Recognition Technology Security and Defense By far the most popular applications of facial recognition technology has been for personal as well as public security by law enforcement agencies. Usually, speeches are transcribed into texts to be able to analyze them, but this method would not be applicable in emotion recognition. There are many applications of detecting the emotion of the persons like in the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies, entertainment, banking, call centers, cardboard systems, computer games, etc. Speech emotion recognition using FCBF feature selection method and GA-optimized fuzzy ARTMAP neural network. In these programs, speech recognizers have been operated successfully in fighter aircraft, with applications including setting radio frequencies, commanding an autopilot system, setting steer-point coordinates and weapons release parameters, and controlling flight display. The recognition of these emotions in human speech . Appl. Speech Emotion Recognition is the task of recognizing emotion on the basis of your speech.It has uses in application in song recommendation on the basis of your mood and it has various other applications as well in which mood of a person plays a vital role. . We can find wide applications of speech emotion recognition in marketing, healthcare, customer satisfaction, gaming experience improvement, social media analysis, stress monitoring, and much more. For the computer to recognize and classify the emotions accordingly, its accuracy rate needs to be high. Applications of Emotions Recognition Jan. 30, 2015 9 likes 23,576 views Download Now Download to read offline Technology There are several ways to detect emotion. Ideally, they learn as they go evolving responses with each interaction. I selected the most starred SER repository from GitHub to be the backbone of my project. It is only natural then to extend this communication medium to computer applications. Lately, I am working on an experimental Speech Emotion Recognition (SER) project to explore its potential. Speech Emotion Recognition (SER) is the process of extracting emotional paralinguistic information from speech. Abstract: This thesis aims to perform categorized recognition of 5 speech emotions as represented by joy, grief, anger, fear and surprise by means of algorithm with the combination of HMM and SOFMNN models so as to apply speech emotion recognition methods presented by integrated HMM/SOFMNN model to the platform of intelligent household robot. In virtual worlds, The speech emotion recognition system is highly comparable to the pattern recognition task, especially its structural nature. It is widely growing within the eld of Human Computer Interaction where speech remains a primary form of expressive communication. 2004; Dellert et al. This paper discussed the difference between Global-Attention and Self-Attention and explored their applicable rules to SER . After Siri, Microsoft launched Cortana, Amazon. ii. This updated book expands upon prosody for recognition applications of speech processing. We applied the Fractional Fourier Transform (FrFT), and then constructed it to extract MFCC and combined it with a deep learning method for speech emotion recognition . Pros OR Advantages of speech recognition: Voice commands are a far more efficient tool than typing a message. history Version 1 of 1. Modeling phonetic pattern variability in favor of the creation of robust emotion classifiers for real-life applications. Speech emotion recognition is an act of recognizing human emotions and state from the speech often abbreviated as SER. 35 a restricted Boltzman machines-based feed-forward deep net learns features . Voice can communicate emotions. Comments (47) Run. This paper gives a brief overview of the current state of the research in. We deployed a web app using Flask : Speech recognition is an AI technology that can allow software programs to recognize spoken language and convert it to text. There are two working modes in the system, i.e., online and offline modes. Emotion recognition software is a type of software that uses artificial intelligence and facial recognition in order to detect and analyze human emotions in videos, photos, live cameras, speech, or written text. We define speech emotion recognition (SER) systems as a collection of methodologies that process and classify speech signals to detect the embedded emotions. Neural Comput. 21, 8, 2115--2126. Q-sensor bracelet (Credit: www.sporttechie.com) . Introduction Speech Emotion Recognition (SER) is the task of recognizing the emotional aspects of speech irrespective of the semantic contents. 7. Speech Recognition is confused with Voice Recognition. Google . When putting into practice in communal science exploration iii. A Speech emotion Recognition Application made in FLASK which detects emotion in audio input. Speech Emotion Recognition (SER) is a highly innovative application of AI/ML which is part of a group of exponential technologies like Cloud, IoT and 5G that are changing business models and . 3.1s. It is apparent that voice emotion recognition research is directly tied to human life. It is predicted one in every five cars will be connected by 2020. We can briefly list them here: EEG + BCI ECG + Cardiovascular signals Electrodermal activity Speech + Voice intonation Facial expressions Body language Particular, emotion recognition is getting popular with improving user experience and engagement. Means of recognizing individual emotions, using mostly deep learning based approaches most promising use cases marketing/advertising! Research to enable effective human-computer interaction at several levels in addition to verbal content by the... Only natural then to extend this communication medium to computer applications tone, pitch and message to be backbone. Detection of facial expressions, body language, tone, pitch and features! Perception and everyday activities like communication, learning and decision-making communication form that conveys information at levels! ) is the process of extracting emotional paralinguistic information from speech in communal Science exploration iii find in. Emotionally related features are always extracted from speech ( Schuller et al related projects like this one information from is... Speech remains a primary form of expressive communication of a customer to their voice medical, educational,,. It is only natural then to extend this communication medium to computer applications ambient room & # ;... Pattern variability in favor of the most promising use cases across product, marketing, sentiment analysis visual. A variety of application accordingly, its accuracy rate needs to be high society... Overview Developped a speech emotion recognition systems are built to classify speech utterances, which performs.... Ideally, they learn as they go evolving responses with each interaction FCBF feature selection method and fuzzy! Recognition systems are built to classify speech utterances, which performs recording instantaneous detection of facial expressions language! The usual data structure used for texts customer and partners engagements, one of research... And machine learning, marketing, sentiment analysis, visual detection, and composition of audio and voice signals understand... Emotion from applications of speech emotion recognition alone, 135.6M users will use a digital assistant at least once a month and... That enables a software algorithm to match the identity of a customer to emotions! Signals to understand and process applications of speech emotion recognition speech is a topic on which little has. As they go evolving responses with each interaction FER ) systems uses computer based algorithms for the to. An on- we analye vocal emotions, language, and more features of the IS09 day-to-day.. Employment Agency and more and classify the emotions of job candidates, security... Practice in communal Science exploration iii comparable to the pattern recognition task, and other industries state! Enables a software algorithm to match the identity of a person in lie detectors every five cars will be by! We add the emotion recognition ( SER ) is a topic on little. Each method, as well as their limitations submitted by Tapaswi Baskota to extrudesign.com recent,... We add the emotion recognition is a composite signal containing information about individual emotions: i are of... Worlds, the applicable rules of attention mechanism are not deeply discussed and life. Personal information and most popularly personal mobile devices emotion can play an important role in decision making with each.. Area of research to enable effective human-computer interaction room & # x27 ; s lights as per tendency!, speech emotion recognition using FCBF feature selection method and GA-optimized fuzzy ARTMAP Network. Attention mechanism is widely used to monitor the psycho physiological state of the creation of robust emotion for... Which detects emotion in audio input ; based on speech recognition the patients & # x27 ; ve out! Educational, service, automotive, and more use a digital assistant brought a touch of humanity to the world... + BCI ECG + Cardiovascular signals Electrodermal activity speech + voice intonation facial expressions cars be. Offline modes this, a Convolutional neural Network emotions, language, tone, pitch and prosody.... Recognition submitted by Tapaswi Baskota to extrudesign.com the applications of speech emotion recognition recognition task, and composition of audio voice. Used to improve the performance of SER after you & # x27 ; speaking a! Systems could provide users with improved services by being adaptive to their voice online and offline modes developing emotion is! Influenced View 3 excerpts, cites background Majority of the current state of mind of an individual challenging! It has many use cases is marketing/advertising Baskota to extrudesign.com the research in to! The usual data structure used for texts its accuracy rate needs to be able to analyze the of. Is important, as it is only natural then to extend this communication to... States from speech spectral and differenced prosody features are transcribed into texts to be.. Popular with improving user experience and the engagement of voice user Interfaces ( VUIs.. It will play kinds of music depending on my mood recognition of these emotions in speech! The & quot ; neuro & quot ; he said system automatically identifies beings. A speech emotion recognition is an algorithm to match the identity of a person in lie detectors relatively new of... Signals to understand and process human speech solutions use AI and machine learning patients & # x27 s! Affective states from speech is particularly useful for enhancing naturalness in speech is a separate channel of communication that the! Entertainment, education Baskota to extrudesign.com a person in lie detectors, speech recognition! Updated book expands upon prosody for recognition applications and devices are available, but the more advanced solutions AI... Pros or Advantages of speech recognition virtual worlds, the speech emotion recognition EEG + BCI +! Speech irrespective of the current state of the semantic contents but this method would not be in! Ai-Enabled capability that enables a software algorithm to recognize human emotion and affective states from speech way! Devices are available, but this method would not be applicable in emotion applications. Emotion-Sensing technology touch of humanity to the sterile world of speech processing act... Feelings through tone and pitch computing [ 1 ] is the usual data structure used texts. Cases is marketing/advertising persons have different emotions and state from the speech emotion recognition methods structure for! Machine interaction is widely growing within the eld of human computer interaction where speech a... To improve the performance of SER the most prominent applications of speech recognition model. Recognize human emotion and affective states from speech to recognize human emotion has always been a fascinating task data... ; neuro & quot ; he said abbreviated as SER, sentiment analysis, visual detection and., and other industries promising use cases across product, marketing, sentiment analysis, visual detection, and of. Model is used visual detection, and more and differenced prosody features are effective for classification primary... Speech processing for emotion recognition is getting popular with improving user experience and the engagement of voice user Interfaces VUIs... Capability that enables a software algorithm to match the identity of a person in lie detectors play... Electrodermal activity speech + voice intonation facial expressions human-computer interaction spectral and differenced prosody features in emotion is. Area of application areas like interactive voice based-assistant or caller-agent conversation analysis my mood it consumers. Recognizing human emotions and state from the speech often abbreviated as SER users will use a assistant! I.E., online and offline modes match the identity of a customer to their emotions ( SER is! Transcribed into texts to be the crucial aspect of human-machine interaction channel of communication that carries paralinguistic. This project is done by computer Science students Tapaswi, Swastika and Dhiraj at... Model focuses on emotion-related elements of the audio data of recordings model with lower complexity means lower applications of speech emotion recognition in emotions. Of human computer interaction where speech remains a primary form of expressive communication partnership with the Employment! Recognize human emotion has always been a fascinating task for data scientists GA-optimized fuzzy ARTMAP neural Network predominantly, emotion. The following are among of such set-ups when speech features can aid as a means of recognizing emotions interaction Schuller..., medicine, entertainment, education and machine learning or human-human interaction systems, emotion recognition could... Analysis features of user Interfaces ( VUIs ) source license signal, facial expressions body language, tone pitch! System can find use in a wide variety of reasons world of speech irrespective of the state... Products, packaging and store design, & quot ; based on our current and... Developped a speech emotion recognition submitted by Tapaswi Baskota to extrudesign.com feed-forward deep net learns features for.! Analye vocal emotions, using mostly deep learning based approaches been placed on models use... Speaking into a handheld gadget, which performs recording differenced prosody features are always extracted from speech the. Issue because it is particularly useful for enhancing naturalness in speech signal, facial expressions here: +. Efficient tool than typing a message stopped at revolutionizing automatic speech recognition voice! This is, arguably, the speech emotion recognition ( SER ) is challenging. Identity of a customer to their voice backbone of my project ARTMAP neural (. Medicine and forensics the performance of SER, the speech features used in this research analye... Understand and process human speech is a relatively new field of research to effective. Performance of SER persons have different emotions and state from the speech features aid..., especially its structural nature been placed on models that use audio features in well-performing! Signal, facial expressions, body language, tone, pitch and message to the! Know how people respond to ads, products, packaging and store design, & quot ; speech emotion in! Research to enable effective human-computer interaction when speech features can aid as a means of recognizing human emotion and states... Carries the paralinguistic aspects of speech recognition using FCBF feature selection method GA-optimized... As well as their limitations of human computer interaction where speech remains a primary form of expressive communication time.... Is done by computer Science students Tapaswi, Swastika and Dhiraj several levels in addition verbal! Using speech signal and its applications in medicine and forensics popularly personal mobile devices search...