Despite being around for quite some time, Automatic Speech Recognition (ASR) continues to advance. 1961 marked the creation of the first ASR device. Our homes were only recently able to become connected through technology.
Many people have had some kind of personal interaction with automated service robots thanks to Apple’s assistant, Siri. Many customer service solutions, including IVR and some Chatbots, use their potential in the modern contact centre.
How does ASR work and what is its purpose?
1. An introduction to Automatic Speech Recognition: what is it?
The primary purpose of Automatic Speech Recognition is to convert spoken audio into text .i.e. “speech to text”. As much as possible, it attempts to translate whether it is about reading or understanding a human’s voice in written form. At the moment, virtual assistants like Cortana and Siri are among the most widely used forms of this technology. ASR is a system that comes into play when you activate your mobile device or home hub with a “Hey, Siri” command.
Basic ASR forms may produce a simple text transcript of an audio recording, but more complex forms rely on technologies such as Natural Language Processing (NLP) and Sentiment Analysis to create more complex transcriptions. Combined with AI technologies such as NLP, ASR acts as a key component of conversational AI – machines and systems that can communicate as if they were human.
While we may not be at the point where we’re unable to distinguish between human or machine conversation, rapid developments in AI technology indicate we’re not far from that either.
2. What is the role of ASR in modern technology?
The mobile revolution is one of the key developments that make ASR both possible and desirable. Our refrigerators, cars, lighting, heaters, and other products all have become technologically advanced with the addition of speech-to-text features.
To enable automatic speech recognition, Microsoft Azure provides tools and services to seamlessly integrate such features into your apps. One of the reasons why most people consider Azure Cognitive Services to be the best cloud-based service is the flexibility it offers.
Flexibility, along with dependable performance, inevitably translates to increased productivity in the B2B world.
There are a variety of deployment methods for the speech to text technology. As an example:
- Messaging apps – Text messages are transcribed from voice recordings by ASR
- Search engines – Searches can be conducted using ASR
- In-car system – By allowing drivers to operate navigation and entertainment systems hands-free, ASR improves safety while ensuring that they can focus on the road.
- Virtual assistants – Using a virtual assistant, you can find information, schedule appointments, and perform basic tasks just by speaking.
Customer service is also using this technology. The current use of this technology is threefold:
- As an alternative to traditional keypad input, IVR – ASR offers callers a variety of choices. By vocalizing their response, users do not have to press a particular number on the screen at the prompt.
- Chatbots – Although, for the most part, chatbots are communicating with their users via text, some incorporate aspects of speech. Chatbots will increasingly engage customers through voice-based interactions as ASR becomes more ubiquitous.
- Speech Analysis – Some organizations review voice recordings to improve AI technology performance.
3. Taking a closer look at ASR to understand it better
ASR must overcome many hurdles for it to be accurate, so when analyzing how it works, we have to examine what they are.
Five distinct questions sum up this information.
- Transcript – What was discussed?
- Identifying speakers – When did each speaker talk?
- Recognition of speakers – Who said what?
- Understanding spoken language – What was the topic of discussion?
- Analyze the speaker’s feelings – How does the speaker feel? What emotions is he trying to convey?
To ensure the success of an ASR system, it is important to note that not all of these questions need to be addressed. ASR tools with limited capabilities can only respond to the first question, while systems with advanced capabilities can interpret emotion and intention in speech. ASR’s complexity and capability increase as the number of these questions it can answer increases.
4. Analyzing how machines perceive the voice
In interpreting a word, computers use several different methods. Language interpretations differ based on the fundamental building blocks they use to construct their interpretations.
Machines can interpret words using any of the building blocks listed below.
Phonemes – A language’s fundamental units of sound. Each of the 44 phonemes in English produces a distinct sound.
- Morphemes – Parts of words that have meaning but cannot be broken down further without losing their meaning (e.g., unhealthiness is created by adding “un” and “health” to a word).
- As Part of Speech – You can interpret the meaning of speech in terms of grammatical groupings. Nouns, verbs, singular or plural, etc., are considered according to their role in the sentence.
- Meaning – Words can be interpreted by machines based on their meaning. It is difficult to do this due to the multifaceted nature of many words, and the fact that meanings can change with context.
Phonemes are the basic units of a language, and ASR systems attempt to break a spoken language down into units based on combinations of phonemes.
Here’s how it works.
- Recording software captures the user’s voice as they speak into a device.
- Wave files are created from the audio recording. Any unnecessary, unwanted background noise from this wave file is then removed.
- Wave files are segmented according to their phonemes.
- Analyzing the chains of phonemes is done by ASR software. It analyzes the probability of certain phoneme combinations to determine whole words by using statistical analysis.
- Statistical analysis continues to be a key part of transcribed sentences, paragraphs, and texts.
An artificial intelligence service by Microsoft Azure called LUIS (Language Understanding) applies machine-learning intelligence to conversational text to predict meaning and provide detailed information based on the text. Through its custom portal, APIs, and SDK client libraries, LUIS offers access to its services.
LUIS offers
- Simplification: LUIS relieves you of any machine learning knowledge you may have or any requirement for in-house AI expertise. Creating your own conversational AI application is as easy as clicking a few buttons. Using quickstarts, or using pre-built domain apps, you can build your custom application.
- Secure, private, and compliant.: Thanks to its Azure infrastructure, LUIS provides enterprise-grade compliance, security, and privacy. All data remains yours, and it can be deleted at any time. When your data is in storage, it is encrypted.
- Incorporation: The Microsoft Bot framework, QnA Maker, and Speech service make it easy to integrate your LUIS app with other Microsoft services.
Part of the Azure Cognitive Services, LUIS offers Speech to text, text to speech, speech translation, voice assistants, speaker recognition, and many more features.
Conclusion
AI is accelerating ASR development at an impressive pace and inspiring entrepreneurs to create endless ways to use the technology through the ability of the technology to ‘learn itself’ with large amounts of data.
One area in which ASR stands to benefit the most is customer service. There is a huge demand for Microsoft cognitive service technologies that allow you to cut costs without negatively affecting the quality of customer service. In this regard, ASR is an invaluable tool for any contact centre seeking to improve customer service on a tight budget.
