Introducing state-of-the-art High Accuracy Speech Analysis services under our VisionAI platform where our customers can add powerful speech analysis capabilities into their own apps! What…
Introducing state-of-the-art High Accuracy Speech Analysis services under our VisionAI platform where our customers can add powerful speech analysis capabilities into their own apps!
What is Speech Analysis
High Accuracy Speech signal analysis can be defined as the process of extracting information related to a speech signal (i.e from a recording). This process is based primarily on the method of speech preparation, the study of which covers a wide range of subjects, from linguistics and articulatory phonetics to signal processing and source coding.
Speech cognition variables exist in different languages, dialects, accents, while the vocabulary of speech is growing day by day. There are more complex variations on the surface of the speech signal in the form of different dimensions, duration, pitch, and speaker variability.
Applications Of Speech Analysis:
There are so many applications of High Accuracy Speech Analysis some of them are given below:
Improving speech signal quality by filtering and separating the noise from the speech segments.
Text To Speech:
It is difficult to synthesize natural speech from text, to make speech perfectly natural with emotions.
Detect sound activity:
Identifying classes in an audiovisual forum where only speech exists, ignoring non-speech and silent classes.
Converting speech gestures to text is still a challenge in different situations, recognizable can be word-dependent or independent.
Identify specific keywords throughout the speech.
Editing a speech, such as changing its mood, accent, or speech by a different speaker.
Distinguish from the speaker and the identity of the speaker:
Directing is dividing the speech signal into classes belonging to different speakers while identifying the speaker to identify who is speaking at a particular time.
Classification of emotional speech:
Identify speech emotions such as happy, angry, sad and anxious.
Audio Source Separation:
Isolation of mixed speech signals such as speech overlapping with different speakers or noise.
Noise in speech and text:
Noise is any unwanted signal that distorts the original signal. Adding noise to speech is very different from adding noise to text.
Gave a speech signal with amplitude [n], where n is the sample index, noise is another signal, w [n] which interferes with speech. The hint of a noisy speech can be seen as:
u[n]=s[n] + w[n]
The plot is to make noise in the time and frequency domain. Noise in speech signals modifies the entire signal and makes it difficult to analyze and extract speech classes. There are speech enhancement algorithms to reduce the noise component and improve speech efficiency.
When the text is punctuated, noise can occur in the form of incorrect and missing words that can either change the meaning of the sentence or make meaningless sentences. For example:
Original text sentence: ‘will we ever forget it’
Noisy text sentence : ‘will we never forget it’
In this noisy text, the noise always changes from the form of the word “ever” to “never”, which changes the meaning of the sentence.
Another form of noisy text: ‘Will we never forggt it’
In the above-mentioned noisy text, the sound of noise has changed from the word “forget” to “forggt”, which makes the sentence meaningless due to the wrong words “forggt”.
Therefore, it can be seen that adding noise to the speech distorts the whole signal while distortion in the text is a matter like missing a letter / word or misspelling it.
Challenges in High Accuracy Speech and Noise Analysis:
Speech and noise analysis applications are quite difficult to solve. External factors that further complicate speech and noise analysis produce a variety of noises, including speech and text. To solve this problem, various different signal processing, neuroscience-based methods, monitoring, and non-monitoring machine learning techniques are explored. Due to the erratic nature of speech gestures, deep learning-based methods have been successful for a variety of applications.
Privacy And Security:
- Your data remains yours. Your audio input and transcript data are not logged in during audio processing.
- Our Speech Service offers enterprise-grade security, availability, compliance, and management.
Following are the endpoints supported by VisionAI platform:
- Make audio searchable by converting it to text with high accuracy.
- Translate speech into 100+ languages. Calling this endpoint will be equal to 2 calls i.e. 1 for Transcription and 1 for Translation.
- Predict the sentiment i.e. Positive/Negative in the speech. Calling this endpoint may be equal to 3 calls i.e. 1 for Transcription, 1 for Translation (if the selected lang code is not supported by Sentiment API) and 1 for Sentiment API.
VisionAI platform will let you add powerful and continuously improving highly accurate deep learning based speech analysis technology in your own apps!
Introducing state-of-the-art High Accuracy Text Analysis services under our VisionAI platform where our customers can add powerful Translation (100+ languages supported) and Sentiment analysis functionalities into their own apps!
Text analysis allows companies to automatically categorize the information from text such as emails, support tickets, product reviews, and survey responses. The best text analysis techniques include Emotion Analysis, Title Detection and Keyword Search.
Businesses want to extract specific information, such as keywords, names, or company information. They even want to categorize text with tags by title, or categorize it as positive and negative.
In other words, if we want text analysis software to perform the required tasks, we need to teach machine learning algorithms on how to learn how to analyze, understand, and derive meaning from text. But how?
The simple answer is to tag text examples. Once a machine has enough examples of tag text, it is able to distinguish and associate algorithms between pieces of text, and it can even begin to make predictions.
Every minute 156 million emails and 456,000 tweets are sent. There is a huge amount of data to implement it, and it is impossible for humans to do it alone.
If machines were made fully responsible for sorting data using text analytics models, the benefits to the business would be enormous.
What is Natural Language Processing?
Understanding natural language allows machines to read text (or any other input such as speech) by mimicking the human ability to understand natural language such as English, Spanish, Chinese etc.
As a technology, natural language processing has advanced over the last ten years, with products such as Siri, Alexa and Google’s Assistant enabling NLPs to understand and respond to user requests.
Applications are in customer care, insurance (fraud detection) and contextual advertising.
Today’s natural language processing system can analyze innumerable text-based data without fatigue and in a consistent, unbiased manner.
They understand concepts within complex contexts, and key language understandings that can extract, or summarize, key facts and relationships.
Unstructured data generated each day, from electronic health records (EHRs) to social media posts, this form of automation has become important for efficiently analyzing text-based data.
Text classification is the process of assigning predefined tags or categories to unstructured text.
It is so versatile and can organize, configure and classify anything to provide meaningful data and solve problems.
Emotions are essential for effective communication between humans, so if we want machines to handle texts in the same way, we need to teach them how to detect emotions and make text positive, negative or non-existent.
This is an automated process of understanding opinions about a given topic in written or spoken language.
For example, companies are able to flag complaints or urgent requests by analyzing emotions, so they can be dealt with quickly – and perhaps a PR crisis on social media can be avoided.
Other uses for rating emotions include assessing brand reputation, conducting market research, and improving product from consumer feedback.
Analyzer models use a wide range of text and natural language technologies from EBORE APPS. For selected languages, the APIs can analyze and score any raw text you provide, returning results directly to the calling application.
Topic analysis is also an example of text classification.
The model can categorize feedback into tags such as Customer Support, Ease of Use, Features, and Pricing. For example:
Detection of intention
The model can also be used to detect intent within the text e.g. to better understand consumer feedback about a product.
From the intention to complain about a product to the intention to buy any product.
Extract the key phrase
Automatically extract key phrases to quickly identify key points. For example, for input text “food was delicious and amazing staff” the API returns key conversation points: “food” and “amazing staff”.
Language Detector automatically categorizes text based on its language. This can be very useful for ticket routing.
For example, if you are an international company, you can route tickets to local language teams that understand them.
Identity of name entity
Identify and classify organizations in your text as people, locations, organizations, date/time, quantity, percentage, currencies, and more.
VisionAI gives you access to pre-trained high accuracy text analysis models that can help you analyze your data right now.
Machine Learning And Natural Language Processing
AI provides systems with the ability to learn from experience without explicit programming and help humans solve complex problems.
Following are the endpoints supported by the platform:
- Translate text into 100+ languages with high accuracy.
- Predict sentiment of raw text i.e. Positive, Somewhat Positive, Neutral, Somewhat Negative and Negative with high accuracy.
- Discover insights such as entities, and key phrases in raw text.
High accuracy text analysis transforms unstructured data into qualitative actionable insights, helping companies make smart data-driven decisions.
Introducing high accuracy Image Analysis technology in our VisionAI platform where our valuable customers can process and infer important information from the image.
Image processing is the process of converting an image into a digital form and doing some work on it in order to get a better image or get some useful information from it.
Image processing systems typically involve the processing of images as 2 or 3-dimensional signals, while pre-set signal processing methods are applied to them.
The two methods used for image processing are analog and digital.
Analog or visual technique of image processing can be used for hard copies such as print outs and photographs. Image analysts use a variety of basic principles of interpretation using these visual techniques.
Why images matter
Social media and the web as a whole are more overlooked, brands cannot rely solely on text when analyzing social media data to better understand their audience.
Here’s what Gartner said about the importance of image analytics:
“We do expect multimedia posts to become the predominant type of post on social media. Even the text that accompanies those posts is getting shorter and shorter… It becomes increasingly important for companies to be able to understand what’s going on in those images.”
Jenny Sussin, VP of Research at Gartner
According to Mary Meeker, more than three billion photos are shared daily on social media.
Many of these images include brand products and logos, but 85% of them do not include a reference to the text of the brand.
Without image analytics, brands miss out on a great deal of social conversation about their brand, products, consumers, and competitors.
For Example take a look at this Facebook post from Roger Federer:
Whether Nike is paying or sponsoring Roger for posting about the brand, image analysis technology can help them know exactly when and how Roger is representing the brand.
This is just one example of the power of this deep learning based technology and why it matters to brands.
The ability to recognize the brand logo in images may seem like the technology of the future, but in reality it is a fundamental function of image analysis.
Image analysis involves incorporating an image into the basic components in order to extract meaningful information.
It may include tasks such as finding shapes, detecting edges, eliminating noise, counting objects, and calculating structural analysis or image quality data.
Image analysis is a broad term that covers a variety of techniques that typically fit into these subcategories.
- Image enhancement to create image for display or analysis.
- Isolate letters and objects of interest.
- Noise removal using shape filtering or deep learning.
- Analysis of the region to extract statistics.
Find Images And Scenes
This section provides information for locating labels in images and videos.
A label or tag is an item, scene, or concept found in an image or video based on its contents.
For example, a picture of people on a tropical beach may include labels such as person, water, sand, palm trees, and swimming objects and outside imagination. Our platform can also track activities such as someone riding a motorcycle.
Vision for everyone
Enhance capabilities to as many users as possible with Image Description feature.
With the help of our platform you can identify labels in images and videos that are important for your business needs.
Developing a custom model for processing images is an important step that requires time, skill and resources. It often takes months to complete. Our platform can easily label your images and videos without you investing your time and effort into developing a high accuracy image analysts model.
Analysing Texts In images
VisionAI’s Text Detection or OCR (Optical Character Recognition) can detect text in photos and videos. It can then convert the detected text into machine-readable text. You can use a machine-readable text detection solution in images, such as:
- Visual Search: An example is retrieving and displaying images that contain the same text.
- Content insights: An example provides insights into the themes found in the text that are identified in the extracted video frames. Your application can find recognized text for relevant content such as news, sports scores, athlete numbers, and headlines.
- Assistance in public safety and transportation: An example is detecting car license plate numbers from traffic camera images.
The blue boxes represent information about the detected text and the location of the text.
Detect Adult/Racist Content
You can use Our platform to detect Adult and/or Racist content in an image, the model will return a score based on the prediction which can then by used by your application based on a certain threshold.
- This will return the score or confidence on the given image whether the image contains the adult and/or racist content for moderation scenarios.
- OCR (Optical Character Recognition) on hand written text with high accuracy, convert text in images containing handwritten texts into machine readable text and make it searchable.
- OCR (Optical Character Recognition) on printed text with high accuracy, convert text in images containing printed texts into machine readable text and make it searchable.
- This endpoint will return the bounding box data for area of interest in the image.
For full information of platform features refer to this interactive documentation.
VisionAI can analyze images that meet the following requirements.
- Image must be presented in JPEG, PNG, GIF, or BMP format
- File size must be less than 4 megabytes (MB)
- Resolution must be greater than 50 x 50 pixels
- Dimensions must be between 50 x 50 and 10000 x 10000 pixels.
Infer important information from your images or videos using our state-of-the-art high accuracy deep learning based image analysis platform.
Introducing high accuracy deep learning based Facial Recognition technology in our VisionAI platform where our valuable customers can train their own state-of-the-art models!
Facial recognition is a science that involves understanding how faces are recognized by biological systems and how they can be imitated by computer systems.
Biological systems employ visual sensors, designed by nature for the environment in which the agent resides.
Computer systems uses different visual devices to capture and process faces, as best indicated by each particular application. Sensors can be video cameras, infrared cameras, or 3D scans.
Our high accuracy facial recognition platform can detect faces in photos, videos and live streams.
When you provide an image containing a face, our application detects the face in the identification aspect, analyzes the facial features, and then returns the percentage confidence score for the face.
Easy to use
Add our powerful face recognition models into your own apps via our easy to use REST based APIs which can be used in language of your choice.
The face rotation found in the pose data is explained. You can use a combination of bounding boxes and pose data to drag the bounding box around the faces shown in your application.The default key marks returned are: Eye Left, Eye Right, Nose, Mouth, Left and Mouth Right.
Quality describes the radiance and sharpness of the face. You will find this useful for comparing faces in photos and finding the best faces.
Optional video processing using edge devices.
EBORE APPS uses deep learning technology to accurately analyze images, find and compare faces.
Our platform offers the following API endpoints which can be consumed in any programming language:
- Add a face to face database and train model, this endpoint will return ‘TrainingStatus’ i.e. Succeeded or Failed.
- Add faces via Excel import feature to face database and train model, this endpoint will return ‘TrainingStatus’ i.e. Succeeded or Failed.
- Recognize faces in given image (base64 encoded image or URL) from face database via trained model.
- Two base64 encoded images or image URLs which contains faces to compare via trained model.
- To check the liveness of given face, Base64 encoded image or URL of image to prevent photo and video replay attack i.e. verify whether person in front of the camera is real.
Head over to our interactive documentation to execute all these endpoints and see them in action without needing any third party REST client!
Facial Recognition Applications
Let’s have a look at some of the applications where this technology can be implemented, here we are only showing some of the use-cases and it’s just the tip of the iceberg.
Access Control And Attendance System
Track the movement of subjects within control and manage employee time just by Face.
Law Enforcement agencies
Law enforcement agencies can track criminals by using this platform.
Quick processing of multiple video streams for video surveillance systems, monitoring large groups of people, and real-time blacklist checks and database searches in cities or within a range of protected areas.
For Residential Purpose
Our Platform is extremely useful for home protection and specially to identify intruders.
Identify member identities to enhance home security and home control management.
Applications are endless!
Our valuable customers don’t have to invest any time and effort in training the models which means don’t have to worry about setting up and investing in training, hosting and inference infrastructure and then continuously updating it, we’ll take care all of it!
Our A.I. models have achieved state-of-the-art accuracy and we haven’t stopped there, we are continuously investing in the technology to make it one of a kind and hence the models keep improving by leaps and bounds.
Why would you consider this platform when there are many alternatives available in the market? Well “Compare the quality yourself” because that’s the only way you’ll get the answer.
Our customers are more than satisfied with the quality we are providing as we believe in this quote:
“Always deliver more than expected.”Larry Page, co-founder of Google
We are not only just providing opinionated solutions in-fact our purpose is to put our customers first and we are always ready to customize our solutions based on the valuable feedback we receive from them.
The platform is currently offering APIs in the following domains:
- Facial Recognition
- Image Analysis
- Text Analysis
- Speech Analysis
- Recommendation systems (coming soon)
- Custom object detection (coming soon)
But we are not just limited to these domains, we are constantly investing in new and bleeding edge A.I. models and we’ll be introducing more in the coming future.
All the APIs management is provided by our central Admin Panel where our customers can keep track of usage.
Explore samples in order to get help on how to use the APIs. Currently the samples are available in following languages:
We provide interactive documentation of our APIs where you don’t need to open a third party client to test the API, you can test any endpoint right inside the documentation!
Let’s Get Started
We are here to help you creating your world changing app and it’ll be a privilege for us to work with you along the way.
“Never give up. Today is hard, tomorrow will be worse, but the day after tomorrow will be sunshine.”Jack Ma, founder of Alibaba Group
So what are you waiting for, Be Inspired, Be Brave and Start Creating!