Top 4 Speech Recognition Challenges & Solutions in 2024

Speech/voice recognition has been around for a long time. However, only recently has it been developed enough to deliver significant value in various areas such as:

• Automotive
• Voice biometrics for security
• Customer service
• Smart home devices

The global voice recognition market was valued at ~$10B in 2020 and is projected to grow to ~$27B by 2026. Even though the adoption of voice recognition technology is fast, developing and implementing it can be challenging.

In this article, we aim to highlight the top 4 challenges companies might face while implementing and developing speech recognition technology in their services and the best practices for overcoming them. 

1. The challenge of accuracy

The accuracy of a Speech Recognition System (SRS) must be high to create any value. However, achieving a high level of accuracy can be challenging. According to a recent survey, 73% of respondents claimed that accuracy was the biggest hindrance in adopting speech recognition tech. 

Word error rate (WER) is a commonly used metric to measure the accuracy & performance of a voice recognition system. WER achieves this by summing the words that the system missed or messed up through an equation:

Source: Wikipedia

Background noise

While trying to improve the accuracy of a speech recognition model, background noise can be a significant barrier. When the system is exposed to the real world, there are a lot of background noises, such as cross-talk, white noises, and other distortions that can disrupt the SRS.

Field specificity

Field-specific terms and jargon can also cause hindrances to the SRS’s accuracy. For instance, complicated medical or legal terms can be difficult for the model to understand and can further decrease its accuracy

How to overcome the accuracy challenge in speech recognition?

The following best practices can help overcome the aforementioned challenges:

• Knowing the user’s environment before developing the model can be beneficial in understanding what kind of background noise the SRS will be required to ignore.
• Try selecting a microphone with good directivity towards the source of the sound.
• Leverage linear noise reduction filters such as the Gaussian mask.
• Build the algorithm to incorporate interruptions and barge-ins while the sound is being input/output
• To overcome the challenge of field specificity, the model needs to be trained with voice recordings from different fields such as healthcare, law, etc.

2. The challenge of language, accent, and dialect coverage

Another significant challenge is enabling the SRS to work with different languages, accents, and dialects. There are more than 7000 languages spoken in the world, with an uncountable number of accents and dialects. English alone has more than 160 dialects spoken all over the world. No SRS can cover all of them. Even aiming for the compatibility of only a few of the most spoken languages can be challenging. 

In the same study, 66% of respondents found accent or dialect-related issues a significant challenge for adopting voice recognition tech.

How to overcome linguistic challenges in speech recognition?

An effective way to overcome this challenge is to expand the dataset and aim to achieve optimum training for the AI/ML model which powers the SRS. The more countries/regions you would like to deploy your SRS solutions in, the more diverse its dataset needs to be.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

3. The challenge of data privacy and security

Another barrier that causes hindrance in the development and implementation of voice tech is the security and privacy-related issues attached to it. A voice recording of someone is used as their biometric data; therefore, many people are hesitant to use voice tech since they do not want to share their biometrics.

For instance, the market for smart home devices is rising rapidly. According to NPR, every 1 in 6 Americans has a smart home device in their homes. Brands such as Google Home and Alexa collect voice data to improve the “accuracy” of their devices, or so they claim. And this makes data collection necessary for improving their product’s performance. Some people are unwilling to let such devices collect their biometric data since they think this makes them vulnerable to hackers and other security threats.

Watch this video to see how smart home devices can be hacked:

Companies also use this data for advertisement purposes. For instance, Amazon stated that it does use customer voice recordings gathered by Alexa, a voice assistant, to target relevant ads to their customers on their different platforms. For instance, if Alexa learns from users’ conversations that they are interested in purchasing a coffee maker, the algorithm learns from it. It will then expose the user to coffee maker advertisements for the next few days. The device basically needs to listen to the user and gather data constantly to achieve this. This is what many users do not like.

Watch how this TED talk explains how smart home devices collect data and the security concerns related to the technology.

How to overcome the security challenges in speech recognition?

We believe that there is no single solution to this issue. The only thing companies can do is to be as transparent as possible and give users the option not to track. For instance, Google offers the users of its Google Home devices the option of tracking and managing what data the device can and can’t collect. In addition, the users can limit data collection from the settings option.

Being transparent about data collection and being aware of the country’s policies regarding biometric data collection can save businesses from expensive lawsuits and unethical practices.

4. The challenge of cost and deployment

Developing and implementing an SRS in your business can be a costly and ongoing process.

As mentioned earlier in the article, if the SRS needs to cover various languages, accents, and dialects, it needs a large dataset to be trained. The data collection process can be expensive, and the training model requires strong computational power.

Deployment is also expensive and difficult since it requires IoT-enabled devices and high-quality microphones for integration into the business. Additionally, even after the SRS is developed and deployed, it still needs resources and time to improve its accuracy and performance.

How to overcome deployment challenges in speech recognition?

To manage the SRS data collection cost, check out this comprehensive article on different data collection methods to find the best option for your budget and project needs.

If the development process is unaffordable, you can consider outsourcing the development or considering ready-made SRSs.

Further reading

• Speech Recognition: Everything You Need to Know
• Top 11 Speech Recognition Applications
• Audio Annotation: What is it & why is it important?
• Top 3 Methods for Audio Sentiment Analysis

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