What is Speech Recognition Dataset?

Speech Data Recognition

We see virtual assistants that use speech data recognition everywhere, like in our mobiles, tablets, TVs, homes, speakers, laptops, and even cars. It may appear straightforward to us now, but there have been numerous failures and dead ends for every innovation in speech recognition. However, between 2013 and 2017, Google’s word accuracy rate increased from 80% to 95%, and it was predicted that by 2020, voice queries would account for 50% of all Google searches.

However, we need speech data collection to develop AI that can translate your voice to text, search it on the web, and translate text to speech. This article will explain what speech data collection is, as well as the key features, algorithms, and use cases. But first, let’s look at…

What is Speech Recognition Dataset?

Speech recognition dataset, also known as speech data recognition, is the ability of a computer programmed to convert human speech into text. While it’s often confused with voice recognition, speech recognition is concerned with converting speech from a verbal to a text format, whereas voice recognition is involved with identifying a specific user’s voice. The process of speech recognition can be broken down into three stages:

Automatic Speech recognition (ASR) is the process of converting audio into text.

Natural Language Process (NLP) uses speech data and the transcribed text to derive meaning.

Text-To-Speech (TTS) converts the text into a human-like voice

What are the key features of speech data recognition?

There are numerous speech recognition applications and devices available, but the more advanced solutions rely on artificial intelligence and machine learning. To understand and process human speech, the program combines grammar, syntax, structure, and composition of audio and voice signals. The AI should, in theory, learn as it goes, changing its responses with each interaction. The best systems also enable businesses to customize and adapt technology to their specific needs, including everything from language and speech nuances to brand recognition. For example:

• Language weighting: Beyond the terms already in the base vocabulary, improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon).
• Speaker labelling: Produce a transcription of a multi-participant conversation that cites or tags each speaker’s contributions.
• Acoustics training: Pay attention to the acoustics of the situation. Train the system to adapt to different speaker styles and acoustic environments (such as those found in call centres like voice pitch, volume and pace).
• Profanity Filtering: To clear speech output, use filters to identify specific words or phrases.

What are the speech data recognition algorithms?

The complexities of human speech have made development difficult. It’s one of the most difficult areas of computer science to master, as it combines linguistics, mathematics, and statistics. The speech input, feature extraction, feature vectors, a decoder, and word output are all components of speech recognizers. To determine the appropriate output, the decoder uses acoustic models, a pronunciation dictionary, and language models. The accuracy rate, or word error rate (WER), of speech recognition technology, is measured. Pronunciation, accent, pitch, volume, and background noise are all factors that can affect word error rate. Speech recognition systems have long sought to achieve human parity, or an error rate comparable to that of two humans speaking. The word error rate is estimated to be around 4% by research, but it’s been difficult to replicate the results on the paper.

To convert speech to text and improve transcription accuracy, a variety of algorithms and computation techniques are used. The following are some of the most commonly used methods:

1. Natural Language Processing: NLP is a branch of computer science- specifically, a branch of artificial intelligence (AI)- concerning the ability of computers to understand the text and spoken words in the same way that humans can. While Natural Language Processing (NLP) isn’t necessarily a specific algorithm for speech recognition, it is a branch of artificial intelligence that studies how humans and machines communicate through languages, such as speech and text. Many mobile devices have speech recognition built in to conduct voice searches (example Siri, Google Assistant or Alexa) or to improve texting accessibility
2. Hidden Markov Models: Hidden Markov models are based on the Markov chain model, which states that the probability of a given state is determined by its current state rather than its previous states. Hidden Markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are used as sequence models in speech recognition, assigning labels to each unit in the sequence, such as words, syllables, sentences, and so on. These labels create a mapping with the input, allowing it to figure out the best label sequence.
3. N-grams: This is the most basic type of language model (LM), in which sentences or phrases are assigned probabilities. An N-gram is a collection of N words. “Order the Pizza”, for example, is a trigram or 3-gram, while “please order the pizza” is a 4-gram. To improve recognition and accuracy, grammar and the probability of certain word sequences are used.
4. Neural networks: Artificial Neural networks(ANNs) and simulated neural networks(SNNs) are types of neural networks that are used in deep learning algorithms. Their name and structure are inspired by the human brain, and they function similarly to biological neurons.
5. Speaker Diarylation: Speaker Diarylation algorithms recognize and segment speech based on the identity of the speaker. This helps programmed distinguish between people in a conversation and is commonly used in call centers to distinguish between customers and salespeople.

What are the use cases of speech data recognition?

There are various use cases of speech data recognition, some of these are:

1. Automotive: By enabling voice-activated navigation systems and search capabilities in-car radios, speech recognizers improve driver’s safety.
2. Technology: Virtual Agents are becoming more and more integrated into our daily lives, especially on mobile devices. We use voice commands to access them via our smartphones, such as Google assistant or apple’s Siri, for tasks like voice search, or through our speakers, such as Alexa, or Cortana.
3. Healthcare: To capture and log patient diagnoses and treatment notes, doctors and nurses use dictation applications
4. Sales: Speech data recognition has numerous applications in sales. A call center can use speech data recognition technology to transcribe thousands of calls between clients and agents in order to identify common patterns and issues.
5. Security: Security protocols are becoming more important as technology becomes more integrated into our daily lives. Voice-based authentication adds a layer of protection.

What can GTS do for you?

We at Global Technology Solutions understand your need for high-quality AI training datasets. That’s why we provide you with different datasets like Voice, Video, Text, and Image. WE have the resources and expertise to handle any natural language corpus construction, truth data collection, semantic analysis, or transcription project. We have a vast collection of data and a strong team of experts to help you tailor your technology to any region or locality in the world.