How to Improve Speech Recognition Technology for Your Business

How to Improve Speech Recognition Technology for Your Business

“Open Sesame!” Ali Baba shouts in “One Thousand and One Nights.” This magical phrase, which is the password to access the cave of hidden treasures, is akin to the modern-day “Hey Siri” or “OK Google.”

The Internet is evolving from the tip of your fingers to the tip of your tongue. One can simply call out to start a voice search or action. The voice assistant listens and responds right away, whether the command is to play the news or get driving directions.

In fact, voice command has become so common that an estimated 128 million Americans use it regularly, which represents 44.2% of Internet users and 38.5% of the total population (eMarketer, 2020).

How Speech Recognition Technology Works

A smart device listens for a command and answers us, which seems to mimic human-to-human interaction. But the process of it isn’t human-like at all. Voice assistant devices are powered by natural language processing (NLP) with deep learning, which is the technology that helps computers understand how humans communicate.

Voice-control devices follow the steps below to process and analyze large amounts of natural language data.

1. A user talks to the voice assistant device using a wake word.

2. The device receives the response in audio and converts it into text, using speech-to-text technology.

3. The device processes the data with NLP technology.

4. The device converts the processed text data into audio, using text-to-speech technology.

5. The device plays the audio data to the user.

The pipeline may seem easy to implement, but it isn’t. Human language is very complex for computers to understand. So the NLP pipeline must help computers recognize the intentions behind phrases they detect through morphological, syntax, semantic, and pragmatic analyses of the human language.

The challenges of implementing NLP are many, as it is a crossover field of computer science, artificial intelligence, and linguistics.

Challenges in Natural Language Processing

Speech recognition technology has grown rapidly in recent years, but it still has room to grow. Voice-control devices with even 90% accuracy may misunderstand neologisms, abbreviations, and context cues.

For instance, smart devices may fail to distinguish “ice cream” from “I scream,” because they can’t divide the speech signal into the appropriate syllable boundaries yet. The phonetic spelling for these words with their syllable boundaries, indicated with a period, would look like this: /ˈaɪs.krim/ and /aɪ.skrim/.

There are many challenges to creating a seamless customer experience in speech recognition technology. Even the simple fact that languages evolve makes it complicated to train AI.

Words or expressions have different meanings depending on the context, and they acquire new meanings over time. This is why language service providers collect and process large amounts of data that reflect natural speech patterns.

When it comes to building a smart device with a voice-control feature, understanding individuals’ idiolects may be the most challenging. The speech recognition technology must accommodate variations in speech habits, such as regional, social, stylistic, and age-graded.

Given these speech variations, the key to establishing accuracy in NLP algorithms is large and diverse datasets. Training datasets that contain various regional and social dialects, background noise, and typical grammatical and word-order mistakes would streamline and improve the performance of a voice-command device.

To sum up, larger and more diverse datasets will result in the more accurate speech recognition solution for your business.

Where to Find the Best Datasets for Your Speech Recognition Solution 

Demand for high-quality speech data is growing as more businesses integrate voice search into their marketing practices. Flitto is the world’s largest crowd-sourcing platform for data collection.

Flitto supports multilingual corpus, speech, and image data to train AI engines in 25 different languages, covering a number of domains including conversational, colloquial, and medical. 

How to Improve Speech Recognition Technology for Your Business

Flitto collects, on average, 3,500 minutes of speech data daily, with 10 million multilingual users and over a million certified translators on the platform.

Flitto builds speech datasets that accommodate businesses’ specific needs, such as an English dataset spoken by non-native speakers, or a Chinese dataset in the Cantonese language spoken by natives. Flitto-provided datasets come with an exclusive right to use, based on the data license agreement with the creators.

It is hard to imagine AI and machine learning in practice without training data. It is essential to train NLP models using diverse datasets to overcome common challenges.

Build your speech recognition solution with Flitto’s datasets to ensure accuracy and a streamlined customer experience.