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Friday, January 27, 2023

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Written by Dr Pradeep Kishore

Infosys BPM experts discuss the role of audio annotations in the creation of intelligent voice-operated devices such as chatbots and virtual assistants.

No one flinches when Alexa counts the value of pi with precision to up to a hundred digits or when she points us to the nearest pizzeria. But our jaws drop when he comes up with some wacky answers to weird questions, like:

“Alexa, who is your best friend?”

‘I have a very strong connection with your Wi-Fi!’

How does a virtual assistant know that ‘pi’ is not a food or a consumer good? Or read ‘articulation’ in the correct context? How do you go beyond semantics to detect humor, satire, sarcasm, and wit and offer case-by-case responses to open-ended questions?

Alexa, Siri, Cortana, and all other voice assistants owe their human-like qualities to audio annotation, a next-generation data annotation technique that makes “listening machines” so smart we even assign them a gender. .

What is audio annotation?

Audio annotation is a subset of data tagging. Audio annotators add metadata to audio recordings, making them machine readable and suitable for training natural language programming (NLP) systems. Recorded sounds are derived from various sources such as human speech, animals, vehicles, musical instruments, or the environment. Data engineers painstakingly segregate and tag audio files and describe them by adding critical semantic, phonetic, morphological, and discourse data. The annotated audio is then fed to the NLP application being trained, allowing it to make sense of the sounds.

Why do we need audio data annotation?

Audio data annotation is vital for industries deploying chatbots, virtual assistants, and speech recognition systems to automate workflows and improve the overall customer experience. These bots need to understand human speech or voice commands in multiple situations and respond appropriately to them.

Let’s consider a common scenario: an angry customer is asking about delivery delays with a voice assistant from a supermarket chain. The bot cannot directly list the reasons for the delay. First, you need to make an apology, and then end the conversation with an offer to make up for the delay. In such cases, it means that the machine learning (ML) model must not only identify the language, but also take into account the dialect, intonation, emotion, and demographics of the speaker. You must not only answer questions, but also acknowledge the speaker’s intentions, address emotions, and suggest workable solutions.

Audio Annotation Types

Audio annotation services vary depending on the AI ​​model being trained. Generally speaking, audio annotations fall into one of six categories:

1. Speech to text transcription: Recorded audio, including speech, sounds, and punctuation, is converted to text. This is crucial in the automatic generation of transcripts and subtitles for businesses, or in technologies that allow users to control their devices with voice commands.

2. Audio Classification: Audio classification works to separate voices from sounds. This type of annotation is important when the AI ​​model needs to distinguish the human voice from ambient noise, such as the sounds of traffic, machines, or a downpour.

3. Voice tagging: It isolates specific sounds from others in an audio file and tags them with keywords. This type of annotation is used in the development of chatbots for specific and repetitive tasks.

4. Expression in natural language: As the term suggests, it is about noting the smallest details of natural human speech, such as dialect, intonation, semantics, context, and emotion. This process is crucial in building interactive bots and virtual assistants.

5. Music Classification: The annotators mark the musical genres, the instruments and the ensembles. This is useful for organizing music libraries and auto-suggestion recommendations for music lovers.

6. Event Tracking: Event tracking is a technique for segregating and annotating sounds generated from conditions from multiple sources, for example, the sounds of a busy street, where each sound component is rarely heard in isolation. This is crucial in use cases where the user has little or no control over the sound sources.

Harnessing growth through audio annotation

The already significant NLP market is slated to grow at a 25% compound annual growth rate (CAGR) in the coming years, generating more than $43 billion in revenue by 2025. The efficiency of applications based on NLP directly depends on its annotation quality: the better the annotation, the smarter the machine.

Whether it’s for customer service chatbots, GPS navigation systems, voice-activated speakers or sound-aware security systems, they are crucial to building machines that not only ‘listen’ but also respond, empathize, entertain , guide or advise. Hence the need for high-quality audio annotation tools and services in the market.

For organizations on the path of digital transformation, agility is key to responding to a rapidly changing business and technology landscape. Now more than ever, it is critical to meet and exceed organizational expectations with a strong digital mindset backed by innovation.

We help clients’ data science teams create high-quality “training data” for AI at scale, using a platform plus human service model in the loop. It helps to focus on strategic priorities like refining and improving the AI ​​model.

Articles under ‘Fortune India Exchange’ are advertorials or advertisements. Fortune India editing team or journalists are not involved in writing or producing these pieces.

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