What is Automatic Speech Recognition How to Collect Audio Data for it?

Glance

virtual assistants for security authentication, we are slowly entering a world in which machines learning systems are able to understand what we are saying.

Alexa, Siri, and Cortana -- we've all experienced these three virtual assistants throughout our day-to-day chores. They are able to help us turn on the lights at our homes, locate websites with information, or even initiate video conferences.

What is Automatic Speech Recognition (ASR)?

Virtual assistants are the applications made by the automatic recognition of speech (ASR). Also called computers that recognize speech, ASR uses artificial intelligence and machine learning algorithms to analyze the human voice and translate it into text.

How to Collect Audio Data to Train Speech Recognition Systems

To maximize the effectiveness in your ASR models It is crucial to have a large amount of audio or speech datasets.

Artificial intelligence is only as effective as the information it is given. It is crucial to collect large amounts of audio or speech data for training an ASR model to achieve maximum efficiency. Here are the steps to follow for Speech Data Collection to efficiently create your machine model for learning:
  • Create a demographic matrix. Take into consideration the following data such as the location, the language, genders and ages, accents. Also, consider the diversity of settings (a bustling street open office or a waiting area) and the use of technology (mobile phone, computer, as well as headsets).
  • Transcribing and collecting the speech of a person. Collect audio and speech recordings from real people to build your model. In this stage, you will require human transcriptionists to keep track of short and long phrases and other key details in the demographics matrix. Humans are essential for the creation of correctly labeled audio and speech datasets to establish a baseline to further develop and application.
  • Create an additional Test set. After you have the transcript you can pair it with audio data in the same format and divide the pair to contain one sentence each. Make the segmented pairs and then extract the random 20% of the data into an array for testing.
  • Train your language model. Develop additional versions of the text that were not initially recorded. For instance, in cancelling orders, you have only recorded the phrase "I want to cancel my order." In this instance you could include "Can I cancel my subscription?" And "I want to unsubscribe." It is also possible to provide pertinent terms and terminology.
  • Iterate and measure. Review the output from your ASR to determine performance benchmarks. Utilize the model that you have trained and determine how well it can predict your test data. Use your model within an feedback loop to fill in any holes and provide the expected output.

Applications of Speech Recognition

Apart from virtual assistants Speech recognition systems are utilized in various industries:

      1. Travel and Transportation

Based on Automotive World estimates that 90% of the new cars that are that are sold by 2028 will feature voice-controlled. Applications such as Apple Car Play as well as Google Android Auto integrate voice data to allow navigation systems to be activated or send messages, or even change the music playlists of the car's entertainment system.

BMW joined forces with Microsoft to acquire Nuance to make Nuance, which Microsoft acquired to power the BMW Intelligent Personal Assistant, which was first introduced in the BMW 3 Series. The AI-powered digital assistant allows motorists to control their vehicle and gain access to information, including the car's entire manual with just the voice of the driver.

     2. Food

Food giants like McDonald's as well as Wendy's have been stepping up their customer service with the help of automated speech recognition. A AI platform converts voice data and delivers them to cooks for cooking. Integration of systems for speech recognition results in faster and more efficient interactions, as well as lower costs for labor.

    3. Media and Entertainment

YouTube's audio AI-based features are expanded to include live auto-captions. This means that creators are able to stream live with captions that automatically appear on below the display. The ASR feature will be available in a variety of languages, to make streaming more accessible and inclusive.


GTS leading Audio and Speech Data Collection Services provider

To be able to comprehend the natural world, machines have to be trained on huge amounts of spoken or written data which have been annotated based on the parts of speech sentiment, meaning. At Global Technology Services (GTS) Here's what we can bring to the table: over a decade of experience gathering and improving speech and text data for machine learning.

The average of 98% for QA in every data-related operation. We design the layout of our teams, empowering them with top-of-the-line tools to facilitate a variety of tasks and workflows. We provide Data Annotation, AI Training Datasets and Robotic Process Automation. With our global reach we are able to effectively execute massive-scale programs worldwide that are specifically tailored to your business's data collection analysis, annotation, and reporting requirements.

Our audio services as well as speech data comprise:
  • Audio transcription
  • Data evaluation
  • Multilingual data collection
  • Analysis of sentiment
  • Review of Collected Reports

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