Speech data labeling helps your innovation interact and understand natural language more accurately.
Otherwise known as data annotation, data labeling is the human-driven demarcation and classification of raw information to make it more functional for machine learning or AI applications.
The more data you label and use to train the model, the smarter it becomes.
For your speech recognition innovation to reach its full potential, it must pass through a series of machine learning processes. One of those stages is speech data labeling. These are steps you simply can’t ignore to make your AI solution as smart and inclusive as possible.
It’s all about figuring out which steps are most effective for your specific needs, and here we’re going to focus on speech data labeling.
Let’s dig in.
What is speech data labeling?
For this, we pair raw audio files with text files containing timestamps to note key audio events. That includes both verbal and non-verbal elements.
For example, let’s say you’re labeling phone conversation data. Within the context of a specific conversation there can be multiple speakers, a baby crying, a doorbell ringing, and a barking dog.
You can label all or none of the above. It all depends on the guidelines for your specific project. And if you don’t know exactly what you need, we can help. More on that later.
Why is speech data labeling necessary?
Around here, we often say “You can’t start with the same thing that we’re trying to train.”
In other words, you have a vision for how you want your innovation to function, but to get to that level of capability, you must teach it.
Speech data labeling increases availability and inclusivity by processing as many demographic combinations as possible:
- Gender
- Age
- Languages
- Dialect
- Accent
- Non-native speaker
That list goes on and includes background noises and sentence structure.
Data labeling makes sense of the pile of speech data you need to make the technology work.
Different Types of Speech Data Labeling
Data labeling is an umbrella term for several specific tasks performed by our data experts. Here’s a brief introduction to each.
Transcription
Transcription is the act of taking the audio files and presenting what is said in text form. You listen and type out what was said and by whom.
There are, of course, automated transcription services on the market, but we believe human speech transcription remains necessary for use cases where we are trying to improve speech recognition accuracy.
For example, we transcribed speech data for Sonos to help them invent multi-room wireless home audio systems. This required wake word data – Amazon’s “Alexa” and Google’s “Hey Google”, for example.
Our team went through each recording to tag the relevant wake words. This labeled wake word speech data tests and refines the wake word recognition engine. The end goal is users of all demographics or dialects have an equally great voice experience on Sonos devices.
Sound and Noise Data Labeling
Audio data labelers categorize clips based on linguistic qualities and non-verbal sounds. This allows for improvements for natural language processing (NLP) in speech recognition, chatbots, text to speech, and voice search.
To train in-car systems to communicate with humans, for example, you require all possible terms, accents, phrases that would be used to communicate in the vehicle.
Additionally, there’s the labeling of in-car noises.
Field data collection is key here because you need occurrences of all the car horns, traffic, backseat talkers, coffee cups being put in the holders and even construction noise that arise while driving.
Sentiment Analysis
Sentiment analysis identifies and classifies emotions or opinions people express in speech or text data.
Say you have hundreds customer reviews of your website or on your Google Business page, for example. You might want a sense of the general attitude of your customer base, or to know whether people like or hate a particular product.
Sentiment analysis allows clients to gather and analyze feedback from customers and employees in real time. Customers give feedback online or on the phone, and we convert that data into valuable insights.
More specifically, this type of data labeling determines whether a person’s attitude towards a particular topic, product, or service is positive, negative, or neutral.
Companies can then target pain points among the consumer base and shift strategy accordingly to build better customer relationships and improve company culture.
Intent Analysis
Here, labelers analyze the need or desire people convey in speech data, and then organize it into several categories.
Let’s say we come across a phone conversation where someone says: “I’ve been saving like crazy for Black Friday. PlayStation 5, I’m coming for you!”
There are no words like ‘buy’ or ‘purchase’ in this sentence, but the intention the person expresses is “I’m going to spend money on a PS5.”
An intent analysis tool would therefore tag the second sentence as follows:
- Intention = “buy”
- Intended object = “PlayStation 5”
- Intendee = “I”
Examples of intent analysis categories include request, command, or confirmation.
Named Entity
Named entity data labeling teaches NLP models how to identify the following:
- Parts of speech – adjectives, nouns, adverbs, verbs
- Proper names – people, places
- Key phrases – target keywords
They first create entity categories, like Name, Location, Event, Organization, etc., and then feed the model with the relevant training data.
For example, we can identify four types of entities in the sentence “Elon Musk purchased Twitter, a company based in San Francisco, and now serves as CEO”:
- Person: Elon Musk
- Company: Twitter
- Role: CEO
- Location: San Francisco
Furthermore, by tagging some word and phrase samples with their corresponding entities, they teach the NER model how to detect entities itself.
Outlining the Data Labeling Process
The first step is to define your needs.
- What is the precise scope of the project?
- How much data do you need?
- What exactly needs to be labeled?
- What’s your budget?
All that information helps us determine the workforce. We typically rely on a combination of gig-economy freelancers, third-party data labeling vendors, crowd workers, and our in-house experts, depending on the needs of your project.
So, if you need high-volume, reliable labeling that doesn’t require native-language experts we go with a vendor.
But if the task can be broken into small pieces and requires no previous experience – wake words or simple voice commands, for example – we crowdsource it.
Finally, if lower costs are more important, we make use of cutting-edge speech recognition tools and combine them with crowdsourced reviews.
The third step is Quality Assurance, and the level of QA depends on the complexity of your project. It’s a constant, collaborative review process based on your specific needs.
Through it all, we look at what’s behind your request, play a consulting role, catch things that haven’t been thought of as a fresh pair of eyes, and provide a single point of contact so you’re free to focus on bigger picture items with respect to your project.
Leave the Data Labeling to Us
The speech data labeling process begins with your needs as the client.
Summa Linguae Technologies is a trusted partner to many of the world’s most prominent emerging technology companies.
We enter each project with a high level of transcription, labeling, and multilingual media monitoring expertise and experience.
Combine that with forethought, planning, and project management, and the end result is the delivery of high-quality annotation you can use.
We’ve developed custom tools and processes that give us the flexibility to collect data to meet your exact requirements.
Contact us today to get started.
