A Data Collection Strategy for Automatic Speech Recognition (ASR)

1. Making Sense of ASR: Understanding Automatic Speech Recognition Meaning
   1. What Is ASR?
   2. Why Do You Need Audio Data for ASR (Automatic Speech Recognition)?
2. Audio Data Collection Challenge in Automatic Speech Recognition (ASR)
3. Top Data Collection Sources in (ASR) Audio Speech Recognition
   1. 1. Open-Source Audio Datasets
   2. 2. Pre-Packaged or Ready-to-Deploy Speech Datasets
   3. 3. Custom Audio Collection Services
   4. 4. In-Person Speech Data Collection
   5. 5. Proprietary Data for ASR (Automatic Speech Recognition)
4. Summary
5. FAQ

As you scroll through stories on Instagram and encounter real-time captions, have you ever wondered how this feature works? Or have you tried obtaining an auto-generated transcript of a song or podcast on Spotify? There’s no doubt that advanced NLP services stand behind this magic, but what exactly is this novel AI technology?

Automatic Speech Recognition, or simply ASR, is an AI-driven technology that can convert human speech into text. This is why it’s commonly named as Speech-to-Text. The rapid adoption of ASR into real-world applications makes us appreciate AI technology even more now, for three reasons:

• ASR enhances communication with real-time transcription.

• This technology automates tasks, leading to increased productivity.

• ASR ensures accessibility by converting spoken content into written form, especially aiding those with hearing impairments.

However, in this article, we’ll start with the basics of the ASR pipeline, beginning with audio data collection. What are automatic speech recognition (ASR) systems? Why do they need audio data? What kind of data do you need for this technology? You'll find all the answers after reading this piece, so keep scrolling!

Making Sense of ASR: Understanding Automatic Speech Recognition Meaning

As humans, we naturally communicate through speech, experiencing no trouble in understanding each other. Aside from language barriers, of course. Nowadays, people are attempting to teach technology to do the same. They train AI to communicate with us through technology using only our voice.

Automatic Speech Recognition (ASR) started with basic systems that could respond to a limited set of sounds. Today, we can witness AI sound recognition solutions that have advanced into sophisticated systems. They can now understand and respond fluently to natural human language.

This evolution is driven by the growing need to automate tasks that involve interaction between humans and machines. As a result, we see a rising interest in ASR technology. Let’s dive deeper into the meaning behind Automatic Speech Recognition by answering the two pivotal questions: what is ASR, and why do we need audio data to make it work?

If you already have a specific audio annotation request, don’t hesitate to reach out to our expert team and get a custom estimate for your project.

What Is ASR?

ASR (automatic speech recognition) is the method of converting spoken words into written text by analyzing the speech wave’s structure. Speech recognition is challenging due to the various signals in spoken language. Regardless, this field is gaining traction, as it is beneficial for many areas. Smart cities, healthcare, education, and many others can benefit from speech-to-text technology for automated voice processing.

ASR aims to provide a solid foundation for deeper semantic learning. It involves using computer tech, digital signal processing, acoustics, AI, linguistics, statistics, and more. Nowadays, ASR is commonly used in various applications like weather updates, handling phone calls automatically, providing stock information, and inquiry systems.

The technology works by using different methods to turn sound information into text through deep learning. This process involves matching the detected speech signal with text. Modern models like DNN, CNN, RNN, and end-to-end models achieve higher accuracy in recognition compared to older hybrid models. Most popular ASR systems use different models like Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and Deep Neural Networks (DNNs). DNNs are crucial in building these systems because they involve special neural network models and improved training and classification methods.

Currently, many ASR-based voice assistants, like Google’s Assistant and Apple’s Siri, can understand how people talk in real-time conversations. They use automatic techniques based on what they hear to recognize human speech patterns. For instance, Google’s Assistant can talk in more than 40 languages, and Siri can handle 35.

Yet, there’s a drawback with deep learning models—they need a lot of labeled training data to prevent overfitting and guarantee accuracy. This is particularly challenging when there isn’t much data available for automated speech recognition tasks. So, the first and crucial step is to collect the appropriate data before training the ASR model. After that, you can choose between an automated data annotation process and a manual one.

Why Do You Need Audio Data for ASR (Automatic Speech Recognition)?

To build an automatic speech recognition (ASR) model, you need a lot of training and testing data. If the collected audio data for speech recognition isn’t good, it can affect the performance of voice assistants or conversational AI systems.

ASR systems are influenced by several factors:

• Diversity of Speakers: Effective training requires a substantial dataset encompassing speech from a wide range of users.

• Articulation in Speech: Optimal recognition occurs in isolated systems when users articulate words distinctly with pauses between them.

• Vocabulary Coverage: The capability of speech recognition systems varies depending on the extent of words they can accurately recognize.

• Spectral Bandwidth: The performance of a trained ASR system is directly impacted by the quality of spectral bandwidth; decreased bandwidth leads to suboptimal performance, while increased bandwidth enhances performance

Therefore, data gathered for ASR system training should involve a diverse set of speech samples from a large number of speakers, clear articulation of words with pauses, and extensive vocabulary size. Additionally, ensuring high spectral bandwidth in the collected audio data is crucial for optimal system performance.

ASR relies primarily on audio data as its core input to transcribe spoken language into written text. The acoustic features within audio signals, such as pitch, intensity, and spectral content, are essential for accurately interpreting spoken words. While the bedrock of ASR lies in audio data, ongoing advancements explore the integration of additional contextual information or language models to augment accuracy. Particularly in linguistically diverse contexts. However, the fundamental requirement for ASR functionality remains speech (or audio) data.

Audio Data Collection Challenge in Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) is designed to swiftly transcribe spoken language into text. This facilitates seamless communication between humans and technology through voice input. To achieve precise transcription of speech, comprehensive training of ASR is required. But first, you need to collect the right type and amount of data to create a speech recognition dataset.

Speech recognition ML datasets (aka speech corpora/corpus) comprise recorded human speech or audio files along with corresponding transcriptions. A well-trained ASR system with a transcribed audio dataset must grasp the linguistic nuances such as diverse accents, pronunciation variations, and distinct speaking styles. Additionally, the system must adapt to factors like background noise, which can significantly influence the clarity and accuracy of speech recognition.

The methods for collecting audio data depend on the model’s algorithm and the purpose of the ASR system. The positive note is that there are different ways to get the specific audio data you need. If you want a generic ASR dataset, you can easily access public speech datasets online. But if you require speech data that fits your specific solution, you have to gather it on your own.

To ensure a high-performing model, trust the data collection process to industry experts. Send your quote request to Label Your Data and get secure audio collection services from a global team of annotation experts.

Top Data Collection Sources in (ASR) Audio Speech Recognition

Creating a big audio and speech collection can make the ASR system perform better. The main challenge here is to get multiple variations of text and speeches for training and testing. Currently, we only have enough training data for popular languages out of the around 7000 languages spoken globally.

Moreover, people have different accents and varied voices, making it tough to build an ASR system that understands everyone. It’s even harder for those who speak multiple languages because their accents are more diverse. The challenge gets even bigger when we consider things like social habits, gender, dialects, and how fast people talk while trying to get enough resources to teach the ASR model.

Hence, preparing audio data for ASR involves not only accurate transcriptions but also detailed linguistic annotation services to enhance the understanding of language nuances. This process goes hand in hand with text data annotation services, contributing to a comprehensive and accurate dataset.

Luckily, there are enough sources to choose from when collecting human audio and speech for an ASR model:

1. Open-Source Audio Datasets

Open-source speech datasets are a great starting point for obtaining audio data for automatic speech recognition (ASR). They are cost-effective, versatile for diverse languages, and well-documented. These public datasets, available online, include notable ones like:

• Google’s Audioset: with over 2 million YouTube videos, offers labeled audio clips categorized into 632 sound types;

• CommonVoice by Mozilla: comprises 9,000+ hours in 60 languages and is continuously expanding through global volunteer contributions;

• LibriSpeech: with 1,000+ hours from audiobooks, the dataset is suitable for North American English but may not cover various accents;

• VoxForge: with over 100 hours, is a smaller but useful volunteer-created option.

For beginners, publicly available data is a suitable choice. However, quality variability, limited diversity, and varying dataset sizes pose challenges for ASR model development.

2. Pre-Packaged or Ready-to-Deploy Speech Datasets

These datasets are existing collections of audio recordings and labels used for training speech recognition systems. They are obtained by vendors or agencies through crowdsourcing and vary in how they’re collected and processed.

Ready-to-deploy speech datasets save time and cost, with convenient availability and potential discounts on specific data categories. Yet, they offer limited customization, as well as lack ownership benefits, cultural diversity, and technical specifications.

Some companies specialize in selling speech data collected through methods like controlled recordings or transcription software. These datasets, often termed off-the-shelf (OTS), are available for purchase.

3. Custom Audio Collection Services

Say you need a scripted speech collection for your ASR model training. For such a specific request, you may consider creating a custom speech dataset. This involves collecting and labeling speech data tailored to your ASR model.

Customized solutions have several advantages. First, they are generally more affordable than an in-house collection and can scale based on your needs. They are precise due to the expert team tackling the task. A company having multilingual support can collect audio data from specific regions or dialects and customize the dataset for specific scenarios. The main drawback here is finding the right partner that can meet all your ASR project objectives.

Label Your Data provides a global team of data collection experts from diverse backgrounds. Our services are backed by a robust QA. Contact us if you need custom speech data tailored to your specific project needs.

4. In-Person Speech Data Collection

Gathering spoken information directly from individuals in a particular environment is referred to as in-person or field-collected speech datasets. This method works best for (ASR) audio speech recognition tailored to a particular population or environment. Here, one needs to define research questions, create a protocol, select participants, obtain consent, and record speech data with specific equipment.

Such audio data collection captures more natural and real-life speech situations. This, in turn, enhances the accuracy and reliability of your ASR algorithm. However, it may be time-consuming and costly, involve ethical and legal considerations, and have a smaller sample size. This potentially limits your model’s generalizability.

5. Proprietary Data for ASR (Automatic Speech Recognition)

The option of using proprietary or owned data involves collecting audio recordings from your own users. This brings a significant benefit of customization for unique populations. As a result, you get a more accurate representation of your users’ language and context.

Plus, ownership and exclusive rights grant flexibility and control over data use and sharing. However, collecting and annotating data can be time-consuming and expensive, especially for diverse or hard-to-reach populations. This method also poses a challenge with privacy compliance and inherent ASR model bias if data is collected from a limited group.

Summary

Indeed, automatic speech recognition meaning goes beyond spoken words. It is the key to transforming speech into meaningful interactions we can now have with machines.

With the right audio data, you can craft a powerful ASR model for seamless and exceptional user interactions. Fortunately, there are enough methods for collecting speech recognition data, depending on the algorithm and system used. Now that you’re familiar with these sources, and their pros and cons, you can choose one based on your ASR model training goals.

FAQ

by Yuliia Kniazieva
on October 19, 2023. Share on LinkedIn Post on Twitter