.jpg)
.webp)
July 30, 2024
7 minutes
There has been more progress and changes in the field of AI speech recognition in the last 30 months than in its first 30 years of existence.
The voice and speech recognition market is booming. As per Motodor Intelligence, it’s expected to value USD 27.155 billion by 2026, at a compound annual growth of 16.8% between 2021-26.
AI speech recognition is gaining new use cases, with ASR (Automatic Speech Recognition) systems popping up in applications that we use daily. ASR is now used in Spotify for transcribing podcasts, TikTok and Instagram to generate real-time captions, and Zoom for transcribing meetings.
Artificial intelligence-based voice recognition has wide-ranging applications. Apart from making technology more interactive, ASR has the potential to boost customer engagement and satisfaction in the telecalling industry. By analyzing the vast amount of voice data available in the telesales industry, automatic speech recognition can boost customer experience, improve agent performance, and provide top-notch services to clients.
That said, AI speech recognition is struggling to match human levels in accuracy, especially for Indic languages. While most industries are focusing on full-scale automation, the accuracy and effectiveness of ASR technologies are constantly evolving with more research in the field.
In this blog, we will explore more about the past, present, and future of AI speech recognition software. Let’s dive in.
Speech recognition allows machines or computer programs to understand and identify human language and convert it into a machine-readable format.
Automatic speech recognition, as we know it today, was initiated in 1952 when Bell Labs created ‘Audrey.’ As a digital recognition system, Audrey could only transcribe numbers for the longest time. A decade later, scientists improved Audrey's ability to transcribe rudimentary spoken words like ‘Hello.’
For decades, automatic speech recognition was powered by machine learning technologies. While it worked well initially, the accuracy and effectiveness of these models soon plateaued. This is when researchers found the scope for speech recognition in artificial intelligence.
A paper published by Baidu Research, Deep Speech: Scaling up end-to-end speech recognition (2014), demonstrated the potential of artificial intelligence to revolutionize speech recognition models.
This paper led to a renaissance in the field of speech recognition. Not only did the accuracy improve compared to conventional models, but AI speech recognition technology also became widely accessible to companies and businesses around the globe.
Also check out, What is Cold Calling?
AI speech recognition uses trained language models, algorithms, and computer processors. The algorithms break down continuous and complex sounds into smaller, understandable parts called phonemes. Phonemes are the smallest units of sound into which human language can be broken down.
Algorithms and language models are trained using vast amounts of data, and the system becomes more precise and accurate with each use.
Here’s the workflow of how AI speech recognition software works:
Looking to enhance your customer support? Check out our guide on Call Center in Bangalore.
Artificial intelligence-based speech recognition technology performs far better than conventional text-to-speech models. Since AI is a dynamic and self-learning technology, the performance of these models will only improve to get closer to human-level accuracy.
Here’s how AI speech recognition technology is revolutionizing voice communication:
“I am sorry, I didn’t get it. Can you please repeat that?”
If you’ve ever used a voice assistant, you know how common these words are.
However, with the incorporation of AI in speech recognition, technology will become smarter and quicker. With exceptionally trained language learning models, voice assistants and conversational AI software will become better at understanding human speech and generating the right output.
Text-to-speech AI models eliminate the need for intermediary stages. With ASR, you can directly translate audio signals into text. These models are easier to build and train. Additionally, they also have a higher potential for accuracy and efficiency.
AI speech recognition allows for the extraction of sentiments from the audio signal. This feature is widely used in the telesales industry. For instance, a company can analyze the emotions expressed during a call between a telesales agent and a potential customer. This data can then improve agent performance and create targeted telemarketing messages that appeal to your customers.
According to research by Accenture, 91% of customers prefer buying from brands that provide personalized services and relevant offers according to their demands.
Artificial intelligence-based speech recognition creates scope for better personalization and customization of customer-centric channels. This can improve conversational AI's performance and boost customer satisfaction.
AI speech recognition can allow telesales agents to provide personalized services to customers to enhance their buying experience. This is done by analyzing past customer interactions with similar personas. A good example of this is Amazon’s Alexa offering customized shopping recommendations based on the frequency of the user’s purchase decisions.
AI speech recognition can enhance the quality of machine-human interactions. Now, we aren’t talking about Iron Man and Jarvis levels yet. However, speech recognition using AI has the potential to smoothen the way humans communicate with machines. With better-trained language models and algorithms, AI speech recognition software can be taught to understand spoken words and their contexts more efficiently.
Automatic speech recognition has been adopted in multiple industries as a solution to streamline work operations, personalize services, and make data-driven decisions. With the growth in market size, this demand will only increase.
Here are the real-world use cases of artificial intelligence in diverse industries:
AI speech recognition can be employed in the telesales industry to streamline operations and enhance customer service.
Artificial intelligence allows telecalling agents to respond more effectively to customer queries and problems. Calls transcribed by ASR can be archived and used for analytical and compliance purposes. Additionally, automatic transcriptions can make it easier for agents to summarize sales calls to help the next customer faster. ASR-generated transcripts can also be used to audit calls and track agent performance.
Here’s a fun fact: As per Juniper Research, by the end of 2024, the number of devices interacting with voice assistants will have overtaken the human population!
That’s right.
By the end of 2024, an astounding 8.4 billion devices will interact with different types of voice assistants. Do you see the potential now?
AI-based speech recognition software can significantly improve voice assistants. This way, more users will find it convenient to use them to achieve regular tasks like browsing the Internet, etc.
Artificial intelligence-based speech recognition also has the power to transform conversational AI.
The market for conversational AI is expected to reach $29.8 billion by 2028. Well-trained ASR models can improve the functioning of conversational AI, allowing it to offer personalized, timely, and empathetic services to potential customers.
Speech-to-text AI can be effectively utilized in the banking and finance sector to support customer queries and provide them with timely details about their accounts. Using an ASR system will allow the bank’s customers to get easy access to information regarding their account balance, current interest rates, and transaction history.
By providing prompt responses, AI speech recognition software will reduce bank customers' waiting time and enhance their overall experience.
One of the most important uses of AI speech recognition is assistive technology.
Smart devices equipped with speech recognition technology can allow people with low mobility to regulate their immediate surroundings. Individuals with visual impairments can significantly benefit from ASR-integrated screen readers that make interacting and navigating through the content more accessible.
Similarly, for those with communication challenges, AI speech recognition can be leveraged to interpret and vocalize messages from others. Auto-generated subtitles can alter how people with hearing or learning disabilities interact with digital content.
While AI-powered speech recognition can have a transformational impact on technology, the journey is not without roadblocks.
Even the best ASR software cannot claim 100% accuracy rates. The error rate in these technologies arises due to multiple factors, from a high number of dialects and accents in human language to a lack of contextual understanding, low input quality, and much more.
Let’s look at the major challenges in the field of AI speech recognition:
Automatic speech recognition models need to understand and identify words along with their context. While language learning models can comprehend words and phrases, they often fall short when understanding the context of audio signals, especially in long conversations.
A lack of context often reduces the quality of output, with irrelevant or incorrect responses that can frustrate users and decrease the quality of their overall experience.
Audio signals processed by AI speech recognition models are filled with overlapping sounds and background noise, which can complicate transcription. This significantly affects an ASR model's ability to generate accurate transcriptions.
There are several ways to mitigate noise using techniques like noise gating, equalization, and frequency cutoffs. However, these methods come with their own set of limitations, challenges, and expenses.
With the rise in artificial intelligence-powered speech recognition systems, public concerns about data security and privacy have also heightened.
ASR models often process highly sensitive audio signals, and ensuring the security of this data is paramount. Therefore, companies employing this software have to navigate rigorous compliance measures, which are often highly costly and complex in nature.
The diversity of human languages and dialects complicates speech recognition. Training an AI model to understand the nuances of different languages becomes tedious.
This is especially true for Indic languages. India is a diverse country with 30 languages and nearly a million native speakers. Further, due to the proximity of their native speakers, these languages have acoustic and semantic overlap. Combine this with more than 19,500 dialects spoken across the country, and you’ll understand the scale of diversity we’re talking about.
This is why, while most ASR systems have a low word error rate for the English language, they are relatively unfit for the Indian market.
The quality of AI speech recognition software for Indic languages was a significant barrier to developing downstream applications like agent training, customer service enhancement, and quality assessment.
This is why SquadStack created an in-house AI speech recognition model tailored to the nuances of the country's diverse languages. This model provides unparalleled accuracy in transcribing these mixed-language calls.
This model has outperformed all other existing solutions, including Google, Whisper, and Amazon, providing unparalleled accuracy for Indic languages.
AI speech recognition is disrupting the technology and telesales industry, and SquadStack is right in the thick of it. Our speech recognition model is dedicated to improving the telesales services for our business partners and the buying experience of their potential customers.
While our primary focus is to solve downstream applications like agent training and quality-call assurance, we aim to develop our ASR model further to reduce the word error rate.
Looking to outsource your telecalling services with our innovative technology and AI-driven solutions? Schedule a demo to learn more about SquadStack now!
AI speech recognition technology refers to the use of artificial intelligence to process and understand human speech, converting it into a machine-readable format. It involves using algorithms and language models to recognize and transcribe spoken words.
AI speech recognition works through several stages, including signal processing, acoustic modeling, language modeling, and decoding. It starts with converting audio input into digital signals, identifying patterns, and understanding context to produce accurate transcriptions.
AI speech recognition is widely used in various industries, including telesales, customer service, voice assistants, conversational AI, banking, and assistive technology. It enhances customer interactions, automates tasks, and provides accessibility features.
The primary challenges include a lack of contextual understanding, low input quality due to noise, privacy concerns, and the diversity of languages and dialects. These factors can affect the accuracy and effectiveness of AI speech recognition systems.
SquadStack has developed an in-house AI speech recognition model specifically designed for the nuances of India's diverse languages. This model offers unparalleled accuracy in transcribing mixed-language calls, outperforming existing solutions like Google, Whisper, and Amazon.
