Speech Recognition Training Dataset

 

Most of you have used Siri, Alexa, Cortana, Amazon Echo, or other types of speech recognition systems to make daily life tasks easy. These voice assistance systems are made to make human life easy and provide us with effective results. You can create your voice assistance system with the help of an AI Training Dataset. These artificial intelligence-powered voice assistants convert verbal queries into interpreted text and understand what the user is saying to come up with an appropriate response. 

If you are planning to build a voice recognition system or conversational AI, you will need plenty of training and testing data. In today's world, you have multiple options to train your model. If you look for generic datasets, there are multiple public speech datasets available online. Many organisations perform speech data collection to improve the efficiency of voice assistance. Let’s have a look at the definition of speech recognition.

What Is Speech Recognition?

Speech recognition is the ability of software to recognize and process human speech into text. Moreover, the difference between voice recognition and speech recognition might seem subjective to many, but there are some fundamental differences between the two. Although both voice and speech recognition form a part of the voice assistance technology, they are performing two different functions. 

Speech recognition is doing automatic transcription of human speech and commands into the text, while voice recognition only deals with recognizing the speaker’s voice. Let’s have a look at the types of speech recognition systems. You can use a speech dataset for 

Types Of Speech Recognition.

You need to have an idea about the speech recognition data before knowing about the types of speech recognition types. Speech Recognition Dataset is the collection of human speech audio recording and text transcription that helps in training the machine learning systems for voice recognition. 

One enters the machine learning transcriptions and recordings into the ML system to train the algorithm and recognize the nuances of speech and understand its meaning. There are many places where you can get the pre-packaged datasets, you should get customised datasets for your projects. The AI training datasets can provide you with effective and practical results. 

Speech Data Spectrum

The speech data spectrum identifies the quality and pitch of speech ranging from unnatural to natural. Here are some of the classifications of this spectrum:

  • Scripted Speech Recognition Data: It is a controlled form of data collection. Here the speakers record specific phrases from a prepared the OCR Training Dataset. They are typically used for delivering commands, emphasising how the word or phrase is said rather than what is being said. 
  • Scenario-Based Speech Recognition: In this type of system, the speaker is asked to imagine a certain type of scenario and issue a voice command based on the scenario. In this way, the result is a collection of voice commands that are not scripted but controlled. 
  • Natural Speech Recognition: It is the best way of speech data collection. At the end of the speech, the spectrum is speech that is spontaneous, natural, and not controlled in any manner. The speaker speaks freely using his natural conversational tone, pitch, tone, tenor, etc. 

These are the components of the speech data spectrum. Let’s now have a look at the data collection components for speech projects. 

Data Collection Components For Speech Projects

There are a series of steps involved in speech data collection which ensure that the collected data is of quality and help in training high-quality AI-based models. Here are some of the things that you need to consider while data collection of components:

  1. Understand Required User Responses: You need to start by understanding the required user responses for the model. You need to develop a speech recognition model, you should gather data that closely represents the content you need. 
  2. Scrutinise The Domain-Specific Language: You require both the generic and domain-specific content for the speech recognition dataset. For example, you call in to ask for an appointment to check for glaucoma. 
  3. Record Human Speech: After gathering the data from the last two steps, the next step would be to involve humans to record the collected statements. It is crucial to maintain an ideal length of the script. A speech dataset can help you with natural voice processing. 
  4. Allow The Recording To Be Dynamic: You can build a speech repository of various people, accents, and styles recorded under different circumstances, environments, and devices. 
  5. Induce Variability In Speech Recording: Once the target environment has been set up, ask your data collection subjects to read the prepared script below the same environment. 
  6. Transcribe The Speeches: After recording the script using multiple subjects, you can process the transcription. Keep the mistakes intact, as it will help you to achieve dynamism and variety in collected data. 

Possible Use Cases Or Applications

Smart appliances, voice application, speech-to-text, content dictation, customer support, security application, and Note-taking for healthcare. The AI training dataset can help you to achieve the peak performance of your voice assistant. Speech recognition gives you a world of possibilities, and the user can adopt voice applications to increase over the years. Some of the applications of speech recognition technology are:

  • Voice Search Application: According to Google, 20% of the searches conducted on the Google app are for voice. According to the predictions, eight billion people are going to use voice assistance by the year 2023. 
  • Home devices Appliances: Voice recognition technology is being used to provide voice commands to home smart devices such as TVs, speakers, and other devices. 
  • Speech To Text: It is a system used in free computing when typing documents, emails, reports, and others. The speech-to-text eliminates the time to type our documents, write books and emails, translate text, subtitle videos, etc. 
  • Content Dictation: It is another use case of speech recognition which helps students and academics to write extensive content in a fraction of time. It is pretty helpful for students at the disadvantage because of blindness or vision problems. 

These are some of the use cases of voice assistance. A speech dataset can help you to train your voice assistance system to bring its highest performance. Moreover, an AI-based speech recognition software needs to be trained on reliable datasets like Video Dataset on machine learning algorithms to integrate grammar, syntax, sentence structure, etc. 


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