Speech recognition technology, also called automated speech recognition (ASR) is applicable to a wide range of industries and new opportunities are presenting themselves constantly. The global conversational artificial intelligence (AI) market is poised to grow from $ 4.8 Billion in 2020 to $ 13.9 Billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9% During the Forecast Period.

Current Use Cases

Speech recognition technology is being used today in industries ranging from call centers, defense and intelligence, secure communication, regulation, compliance, banking and finance, medical and pharmaceutical, and even voice recognition tools hosted offline. ASR tools assist with asset management and surveillance, and help remote teams to capture data while ensuring security.

However, given the dynamic nature of the variety of scenarios where ASR models are and can be used, training the model to recognize context and accents is crucial. With teams working remotely, globally, the languages they work in may not be uniform in nature, and in order to provide accurate records the ASR model needs to be able to distinguish among different accents, domain vocabulary, and even intuit sentiment.

How Important is Accuracy?

To begin with, accuracy is generally measured by word error rate (WER). The higher the WER, the more mistakes have been made in the ASR model’s interpretation of a given audio file. WER is the baseline metric for testing, and is consistently improving in most ASR models as more languages and accents are integrated.

While most enterprises judge an ASR service provider on their WER, there are many more aspects that should be considered. While WER is important, and an industry standard, those in the ASR market should also consider how well a model actually understands the speaker’s intention. Punctuation, sentiment, speaker ID, reader ID, all of these are factors that should also be considered when choosing the right service provider.

Models are developing and improving to consider additional types of inputs, such as video context, facial recognition, images, and the platform being used itself. The ability to deliver improves as it include the flexibility to understand more than just voice or speech.

Workflow Automation

ASR technology is revolutionizing enterprise by enabling workflow automation. As an example, distribution supply chains were significantly disrupted due to the COVID-19 pandemic. It exposed the broken links in supply chain and highlighted how outdated networks actually are. Whereas product and logistical data has traditionally been done with paper and spreadsheets, companies like Venzee are using artificial intelligence to automate data transmission in real time to streamline the entire supply chain network, thereby getting you your product description on a retailer website, and ensuring your product arrives on time and as expected despite disruptions in the supply chain.

Automatic workflows also allow for the facilitation of data analysis. Data collection is nothing new, but through the use of ASR, enterprises can collect massive amounts of internal and external data that is easily searchable. Said data can be used to monitor employees, and tailor products and services to customers. It can also be used to ensure compliance. However, in order to comply with privacy regulations, it is in the interest of large enterprise to license their own, offline product that ensures their data remains their own.

Conclusion

Overall, transcription has always been a core driver of data collection. Today, with the way business is shifting from the office to the home office, demand for more data is increasing. Automation is the solution. ARS models can help businesses scale quickly without losing accuracy, and maintain control of their data. AI models can learn from transcripts to improve customer service, compliance, and ultimately add value to any organization’s bottom line.

At Wiip, we provide a compete artificial intelligence service experience. We create datasets, build models, and train teams to manage and maintain those models. We’d love to hear about how you are using speech recognition at your organization. Get in tough to let us know, or leave a comment below!

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