The Importance of Diversity in Speech Recognition Datasets

 

If you’re using Siri, Alexa, Cortana, Amazon Echo, or other voice assistants in your everyday life and you’d agree that Speech recognition dataset has become an regular part in our daily lives. These voice assistants powered by artificial intelligence transform the spoken words from users to text, and interpret and interpret exactly what they are hearing in order to determine the appropriate answer.

There is a requirement for high-quality data collection in order to create solid models for speech recognition. But, the creation of speech recognition software isn’t an easy job — specifically because the transcription of human speech in the entirety of its complexity like the rhythm of accent, pitch as well as clarity is a challenge. When you add emotion to this complicated mix it becomes quite a task.

What is Speech Recognition?

Speech recognition software’s capability to recognize and convert humans’ speech to create text. Although the distinction between speech recognition and voice recognition could be confusing to some but there are some basic distinctions between them.

While both speech and voice recognition are a component of the technology used to create voice assistants however, they serve two distinct tasks. Speech recognition is a method of automatic transcription from human commands, speech while voice recognition is only concerned with recognizing the voice of the speaker.

Types of Speech Recognition

Before we dive into the various speech recognition models Let’s take a short review of speech recognition information.

Data on speech recognition is made up of audio recordings made by humans and text transcription that assist to train machine learning systems to recognize voice.

The transcriptions and audio recordings are incorporated in the system of ML, so that the algorithm is trained to discern the subtleties of speech and comprehend the meaning behind it.

Although there are plenty of sites where you can obtain free datasets that are pre-packaged however, it is better to obtain custom data for your needs. You can choose the size of your collection and requirements for audio and speakers and the language of your choice by acquiring an individual dataset.

Speech Data Spectrum

Speech data spectrum is a way to determine the pitch and quality of speech, ranging from natural to non-natural.

Speech recognition data that is Scripted

The name itself suggests that scripted speech is a controlled kind of data. The speakers write specific phrases using a pre-written text. They are usually used to deliver instructions, focusing on the way in which the word is spoken, not what is actually being spoken.

Speech recognition that is scripted can be utilized when developing an assistant for voice that will be able to recognize commands that are issued with different accents of speakers.

Speech recognition based on scenarios

In a speech that is based on a scenario it is the responsibility of the speechmaker to envision the scenario in which they will give a voice command according to the situation. The result is a set of voice commands that aren’t written, but are controlled.

Audio datasets based on scenarios is needed by designers who are trying to build an instrument that can comprehend everyday speech and its many subtleties. For example, asking directions to locate the closest Pizza Hut using a variety of questions.

Natural Speech Recognition

At the top of the spectrum of speech are speech patterns that appear natural, spontaneous and uncontrolled in any way. The speaker can freely speak by with his natural tone and language, as well as pitch and the tenor.

If you are looking to develop an ML-based program using multi-speaker speech recognition using an unscripted, or a conversational speech dataset is a good choice.

Data Collection components to support Speech Projects

Speech Data Collection A series of steps that are involved in the collection of speech data ensures that the data collected is of high-quality and aids in the training of high-quality AI models.

Learn to recognize the user’s required responses

Get started by understanding the needed responses from the users to the model. To build a speech recognition model you must collect data that closely resembles the content you want to represent. Take data from real-world interactions to better understand user interaction and their responses. If you’re building an AI-based chat agent examine chat logs, recordings of calls and dialogue box chat responses to build data.

Review the language of the domain

You need both domain-specific and generic content for a speech recognition database. After collecting general speech data, it is important to go through the information and distinguish the generic from specific.

For instance, customers could make a phone call to request an appointment to look for glaucoma at an eye center. Requesting appointments is generalized term, whereas the term glaucoma is a domain-specific.

Additionally, when training a speech recognition model, be sure to make it recognize phrases, not individual words.

Record Human Speech

After obtaining data from the earlier two steps The next step would include obtaining human beings to record the data collected into a database.

It is vital to ensure an appropriate length of script. If you ask people to read longer than 15 mins of content can be detrimental. Keep a minimum of 2–3 seconds between each recorded sentence.

Make sure that the recording is fluid

Create a speech repository that includes different people, accents and styles recorded under various conditions including devices, locations, and conditions. If the majority of users are likely to be using the landline and mobile phones, your speech collection database must have an adequate representation that meets the requirements.

Variability in Speech recording

After the environment for data collection is in place and you have your subjects who are collecting data to read the script in the same setting. Make sure that the participants don’t worry about any errors and to keep the script as natural as you can. The goal is to get an entire group performing the dialogue in the same space.

Transcribe the Speeches

After recording the script by using different subjects (with errors) You should continue by transcribed. Be sure to keep your mistakes in order to ensure that you to achieve dynamism and diversity in the data you have collected.

Instead of having human beings transcribe the whole text word-for-word You can use an engine that converts speech into text to perform the transcription. However, we suggest that you use human transcribers to rectify errors.

Create a test Set

The creation of tests is vital since it’s a prelude towards the model of language.

Create a pair of speech and text, and cut them into segments.

After collecting the data After that, you can extract a sample of 20% of the data, which is an experimental set. This is not the set used for training, however, the data you extract will inform you whether the model that was trained can transcribe audio that it hasn’t been taught on.

Create a model for language training and test

Create the speech recognition model by with the domain-specific expressions and any additional variations that are needed. After you have created the model, it is time to begin measuring it.

Use the training model (with the 80% of selected audio tracks) and compare it to the testing set (extracted 20% of the dataset) to determine if the predictions are accurate and their reliability. Look for patterns, mistakes and pay attention to factors in the environment that could be corrected.

Speech Recognition Use Case

Speech Application and Smart Appliances Speech to Text customer support, content dictation Security software, Autonomous vehicles Note-taking in healthcare.

Speech recognition can open up a vast array of possibilities. The users’ use of these applications has grown over time.

The most common uses of technology for speech recognition include:

Voice Search Application

According to Google the app, around 20% of searches made through the Google application are conducted using voice. 8 billion people are expected to make use of the voice-based assistant in 2023. This is a significant increase over the forecast of 6.4 billion by 2022.

Home Devices/Smart Appliances

Voice recognition technology is utilized to give commands using voice to smart home devices like televisions, lights and other devices. 66% of people across Germany, the UK, US, and Germany reported using voice assistants when using their smart devices and speakers.

Text to speech

Applications that use speech-to-text are used to assist in free computing while typing documents, emails reports, emails, and more. Speech-to-text eliminates the need to write documents writing books and emails or subtitle videos. It also allows you to translate texts.

Customer Support

Speech recognition systems are utilized extensively for customer support and service. Speech recognition systems aid in offering customer support solutions for 24 hours a day at a reasonable cost with a small amount of agents.

Content Dictation

Content dictation is yet another speech recognition application that can help academics and students create a large amount of content in just a little time. It’s a great option for students who are disadvantaged because of vision impairment or blindness.

Security application

Voice recognition is widely used to protect and authenticate to identify distinctive features of the voice. Instead of requiring the user to identify themselves by using personal data that is stolen or abused, the use of voice biometrics can increase security.

Additionally, the use of voice recognition for security reasons has increased customer satisfaction because it removes the lengthy login process as well as duplicate credentials.

Note-taking in health

Medical transcription software based on speech recognition algorithms effortlessly captures notes from doctors’ voices as well as diagnoses, commands and symptoms. Note-taking for medical notes improves the efficacy and speed of care in the health sector.

Are you working on a speech recognition idea in your mind that will improve your company? The only thing you’ll need is an individual speech recognition data set.

A speech recognition application based on AI must be trained using solid datasets of machine learning algorithms that integrate syntax grammar, sentence structure, grammar emotion, and the subtleties that human voices convey. The most important thing is that the software must constantly learn and adapt to each interaction, growing with each interaction.

How GTS can help you?

Global Technology Solutions is a AI based Data Collection and Data Annotation Company understands the need of having high-quality, precise datasets to train, test, and validate your models. As a result, we deliver 100% accurate and quality tested datasets. Image datasets, Speech datasets, Text datasets, ADAS annotation and Video datasets are among the datasets we offer. We offer services in over 200 languages.