5 Text Annotation Services for Voice Assistants

Article by Limarc Ambalina | September 25, 2019

Voice assistants are becoming more and more prominent in today’s society. They can be found in smart speakers, mobile phones, and even autonomous vehicles. Strong voice assistants allow users to speak to them freely or casually. However, before voice assistants can appropriately respond to a question or command, they first have to understand the words being spoken. To improve the speech recognition and speech synthesis of your voice assistant, you will need annotated text data to train your algorithm. 

The appropriate text annotation service for your project will depend on what algorithms you are looking to train. To help you build an industry-leading voice assistant, below are five text annotation services. Alternatively, if you’re interested in building your own text datasets, consider using our data annotation platform and check out this list of text annotation tools.

 

5 Text Annotation Services for Voice Assistants


1. Intent Classification and Intent Variation

Text Annotation Services - Intent Variation & Intent Classification

Voice assistants are often used as information repositories. However, users can ask the same question or request the same information in a variety of ways. Intent variation tasks annotators with providing multiple phrases or words for a single intent. By creating a training dataset using intent variation, you teach the voice assistant how to understand different ways of phrasing the same request. 

Along with intent variation, you can also employ intent classification. This service is used to classify user queries into appropriate categories based on how you want the voice assistant to respond. With accurate intent classification datasets, you can train your model to access the correct responses faster. Quicker responses lead to a better user experience of your voice assistant.

 

2. Machine Translation Quality Evaluation

Users may require your voice assistant to translate, listen to, and understand multiple languages. In order to cater to larger markets, voice assistants often utilize machine translation engines. 

Text Annotation Service - Machine Translation Quality Evaluation

However, machine translation is still far from perfect. By evaluating the quality of your machine translation engine, you can improve machine translation in your voice assistant. In addition to evaluation, professional human translators can retrain your machine translation model. To do so, human translators assess your machine translations and correct errors. Using the corrections, your machine translation engine to avoid similar errors in the future. 

 

3. Part of Speech (POS) Tagging

Text Annotation - POS Tagging

POS tagging deals with labelling the different function words within text data. It is one of the most important tasks for the processing of text data. With function words annotated in your text data, your voice assistant can learn how to properly understand commands and questions. 

POS tagging aims to help machines understand language at the most basic level. Therefore, it is used to create training data for voice assistants, chatbots, search engines, and other NLP solutions. 

 

4. Phonetic Annotation

Phonetic annotation is simply the labelling of pronunciation in speech. For voice assistants and text-to-speech models, phonetically-annotated datasets teach machines how interpret and produce natural language.Text Annotation Services - Phonetic Annotation

With phonetic annotation, human annotators label the stress, intonation, pronunciation, and natural pauses within text. By teaching the AI model the way speech sounds, you can improve both speech recognition and text-to-speech functions in your voice assistant. 

 

5. Text Classification

Text classification is the categorization of text into intuitive categories or classes. By dividing text data into the appropriate sections, the AI model can understand queries quicker and access the appropriate categorized information to respond to the user faster. 

SENTIMENT ANALYSIS

Text classification can come in many forms and is used to train numerous NLP models. One of these forms is intent classification (mentioned previously). Another is sentiment analysis which is the classification of text based on the emotion, sentiment, or opinion within it. Teaching AI models to learn the sentiment within text can help voice assistants give an appropriate response. Sentiment-annotated datasets can even help more advanced NLP models respond with sympathy.

 

Above were five text annotation services which can help train voice assistants. Hopefully, by finding the correct service for your project, you can improve various aspects of your voice assistant. There are more training data services for voice assistants outside of text annotation. Learn more about how Lionbridge can help improve your voice assistant and other NLP solutions.

 

Multilingual Text Annotation Services

Lionbridge provides professional text annotation services in over 300 languages.
Some of our most popular languages include:

  • Chinese text annotation
  • Italian text annotation
  • Dutch text annotation
  • Japanese text annotation
  • French text annotation
  • Portuguese text annotation
  • German text annotation
  • Spanish text annotation
Looking to outsource text annotation?
The Author
Limarc Ambalina

Limarc writes content for Lionbridge’s website as part of the marketing team. Born and raised in Canada, Limarc’s love of Japanese pop culture brought him to Japan in 2016 and living in Japan has been his dream come true. Apart from Lionbridge content, you can catch Limarc online writing about anime, video games, and other nerd culture.

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