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Automatic Speech Recognition and Natural Language Processing Solutions

Regardless of the scenarios, companies can obtain potential business opportunities by using AI audio and language processing technologies. With the rapid development of this technology, AI technology is expected to play a more significant role in the interactions with enterprises. If done correctly, this technology will improve the customer experience and business processes, thereby benefiting both sides.

Speech Recognition Technology

ASR (Automatic Speech Recognition) uses speech as the target. Through speech signal processing and pattern recognition, machines can automatically recognize and understand human spoken words. Voice recognition technology allows devices to convert voice signals into corresponding texts or commands. Speech recognition is an interdisciplinary subject that involves a wide range of topics. It is closely related to acoustics, phonetics, linguistics, information theory, pattern recognition theory, and neurobiology.

Automatic Speech Recognition (ASR) data usually contains noise, causing machines to misunderstand specific words or phrases. Human speech occurs naturally and has no script — we often use words that have nothing to do with our intentions when we speak. Hence, there are many unnecessary words in a sentence, which will affect the interpretation. The wording is also very different, depending on where people come from, what kind of growth environment and experience they have.

When we looked at the statistics of the noise data, we found that in an average of 53% of the cases, the AI was either correct or made small mistakes. In 30% of the cases, AI made small mistakes. In 17% of the cases, AI made a significant mistake. It shows that noisy data is still a problem when launching conversational artificial intelligence.

Generally, conversational AI will perform the following series of events in an interaction with a person:

• Speech to text conversion: AI converts the original audio file of the customer’s speech into text.

• Natural Language Understanding (NLU): AI analyzes and processes text to create actionable instructions.

• Content relevance: AI comes back the best information that can help customers.

Natural Language Understanding (NLU)

1. Clear intention: What is the goal of the human subject? For example, “Where is my order?”, “View list” or “Find a store” is all intents or purposes.

2. Corpus collection: data must be collected, analyzed, and verified under different utterances. In many scenarios, different words refer to the same target. For example, “Where is the nearest store?” and “Find a store near me” are different words with the same intent.

3. Keyword extraction: This technology is used to analyze the keywords in the utterances. In sentences like “Is there a vegetarian restaurant within 3 miles of my home?”, “vegetarian” is the type entity, “3 miles” is the distance entity, and “my home” is the reference entity.

Real-life Applications

Using audio, voice, and language processing to solve real-life problems can optimize user experience, reduce costs. It allows enterprises to shift their focus to higher-level processes. Some solutions in this field have applied in our daily lives. The following are examples:

• Virtual assistants and chatbots

• Voice search

• Text-to-speech engine

• In-vehicle command

• Speech to text

• Enhanced security through voice recognition

  • Telephone voice navigation
  • Translation service

Customized dataset

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.

ByteBridge, a human-powered and ML-powered data labeling tooling platform

ByteBridge is a data labeling SaaS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.

Accuracy

  • ML-assisted capacity can help reduce human errors by automatically pre-labeling
  • The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
  • Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
  • All results are thoroughly assessed and verified by a human workforce and machine
ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

Cost-effective

A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.

End

If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.

Please feel free to contact us: support@bytebridge.io

source: https://www.jianshu.com/p/c37fc406ac4d

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ByteBridge

ByteBridge

A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. — https://bytebridge.io/#/

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