Voice Data Collection Challenges (And Why We Overcome Them)

Voice data collection presents certain challenges but leads to exciting use cases that show why it’s important to power through.

Data is the biggest asset in today’s super competitive world for speech recognition models. Speech recognition can be helpful for almost any organization to scale its services or products.

Voice data collection is unlike the traditional methods of gathering data, though. It involves different techniques where several aspects need consideration.

Let’s look at some common challenges and exciting use cases that show why it’s important to power through.

Voice Data Collection Challenges

1. Varying Languages and Accents

Demand for smart home devices is on the rise globally. Such devices require huge amounts of speech data, including spoken words and statements in different languages and accents.

For example, Amazon Echo offers more languages and locations than Google Home as they have been in the industry longer. In terms of release, Google Home was launched 6 years back, but it is offered in only 11 countries.

One reason behind the limited scope is the inability to expand audio datasets with different languages and accents.

2. Cost of Collection

Speech data collection can be costly and exhausting, depending on the project’s scope.

To improve model accuracy, the spoken audio data size needs to be sufficiently large. With the expansion in size, the cost of speech data collection also increases.

Hiring collectors and using voice recording and storage equipment also contribute to the expense.

3. Longer Timelines

Collecting and recording audio takes more time than image data. Why? Because you record audio in real-time, so it requires longer collection times.

Additionally, it’s not as simple to determine the quality of an audio file versus the quality an image file.

Images can be glanced over to determine quality but an audio QA analyst might have to listen to the entire audio file before approving it for use.

Therefore, this procedure may take several months to complete the dataset.

Gathering speech recognition datasets becomes more time-consuming if the data being captured is:

• In different languages, dialects, and accents
• A noisy speech dataset
• Uncontrollable background noise
• Is of various sizes, resolutions, and formats
• It consists of variations in the speech (like emotions)
• Composed of different voices (male/female speakers, high/low pitch sounds, etc.)

Why must we overcome these voice data collection challenges? Consider these exciting use cases.

How Voice Data Collection Fuels Innovation

Advanced fields for the latest neuroscience research focus on voice-based conversational agents that can learn from the environment and interact with humans mimicking their behavior dynamically.

So, while calls were used to onboard call center employees in the past, they can now be used to train AI models that accomplish several tasks.

Conversational Sales Agents

The conversational sales approach works for clients ready to confirm the deal or close to making a purchase. The conversational sales agent has an active role and one-on-one interaction with the customers in the process.

Speech data sets or call recordings can help sales agents understand customer needs and reflect on ways to improve their services.

Moreover, they can observe the trends, evaluate the current practices, and automate the processes to improve sales.

Call Center Accent Correction

Recording calls is an effective method to identify the exact department or component of your customer service strategy that needs improvement.

It lets you visualize the performance statistics and run regular tests to understand the weaknesses.

You can also determine the employees who need support and training to improve their performance.

Additionally, call centers can work on accent correction of agents by reviewing the sound recordings with personal details.

Intelligent Virtual Assistants

Integrating the latest tools and techniques is the key to enhancing performance and staying updated with the changing trends.

Chatbots are the popular AI models used by organizations big or small, for example. Chatbots improve performance, boost productivity, accelerate sales, increase accessibility, reduce cost, and lighten manual labor.

Big names in the industry, such as Amazon and Accenture use chatbots and purchase call center data for system enhancement.

Other well-known firms like Sephora, Spotify, and Lyft also use chatbot services to help customers with a world-class experience.

Customer Dispute Agents

Tracing the source of a disagreement is essential for resolving it. Agents involved in heated discussions must be unplugged to end conversations for some time until they receive complete training.

Listening to human speech on recorded calls can help management see where the fault lies. If the customer was wrong, listening to a common voice on such calls can help reach better resolutions.

Sustainability Consultants

Sustainability consultants advise organizations on how to produce and deliver their services using sustainable methods. They perform planning, verification, audits, and testing to develop policies that promote sustainability.

Using modern recording tools, consultants can support their planning, structure, and execution. Companies can then boost product sales, conduct surveys, design policies, and make informed decisions.

AI Content Creation

AI content creation is a recent phenomenon but AI engines use all sorts of data to generate engaging content in different tones.

Writing tools like Jasper, SurferSEO, Rytr, etc process text data to help you write compelling pieces. These can use emotional speech to capture user attention in youtube videos.

ChatGPT, probably the most popular AI app, can help you compose emails, blog posts, poems, and writing code. With these AI content creation tools, you can automate your efforts and save some time and money.