How is audio annotation used in AI?
Virtual assistants like Amazon’s Alexa or Apple’s Siri use audio annotation to understand and respond to user queries. The speech recognition software used by these virtual assistants must be trained with a large amount of data to understand the user accurately. This data is typically annotated so the machine learning algorithms can learn from it.
In-vehicle navigation systems also use audio annotation to provide voice guidance to drivers. The system must be able to understand the driver’s speech to provide accurate directions.
Call center responses are another area where audio annotation provides accurate responses to customer queries. The call center agents must be able to understand the customer’s speech. This data is typically annotated so the machine learning algorithms can learn from it.
Implementing audio annotation in a project can be a daunting prospect, particularly for organizations with no experience in the field. That’s why many organizations choose to contract outside help. Many of them choose to use Sigma.ai to help guide them by following the Sigma process.
Sigma.ai audio annotation workflow
- Project Analysis. The Sigma process begins by analyzing the project, discussing details, and developing a project proposal.
- Project Manager Assignment. From there, we assign a project manager to the project based on their skills and expertise. We also form a project advisory committee (PAC) with internal experts in the project area. They discuss the project in detail with the client to disambiguate any issues and review the project requirements.
- Guidelines & Requirements. We discuss the nuances of annotation guidelines with the client and review project requirements. This also involves addressing objectives, metrics, deadlines, deliverables, and security and privacy needs.
- Tools & Procedures Setup. Once details are confirmed, we set up, build, and optimize the tools and procedures for the project. Then, we refine the project definition to improve the quality of the output.
- Comprehensive Test: Annotators use the adapted tools and procedures to annotate or collect sample data, including for quality assurance (QA). The PAC produces a report outlining the results and any suggestions. The clients review this report and sample data. Based on their feedback, we determine if there’s any applicable retesting or retooling.
- Client Feedback. We optimize the tools and processes based on the client’s comments. If the client has updated the guidelines, the tools are retested.
- Annotation Collection at Scale. We bring experienced annotators as part of an iterative process to produce data at scale and improve the performance of the AI.
- Quality Assessment. We produce a final quality assessment report and deliver it to the client at the end of the project.
Because Sigma assigns a specific team of experts built for each project, it has been shown that their expertise can help increase productivity by 25% and project quality by 32% on average.
Why is audio annotation important now?
Audio annotation is widely used in AI projects, particularly those that involve interacting with consumers, such as call center automation, voice biometric security systems, and virtual assistants. Conversational AI allows consumers to ‘talk’ to devices in much the same way as they would to another human being. Audio annotation is particularly important in the modern-day due to the prevalence of devices like cell phones and tablets with microphones and the superior user experience offered by enabling speech interaction for virtually everything.
Growth in AI markets dealing with virtual assistants continues unabated, generating $3.9 billion of revenue for enterprises in 2020 with a compound annual growth rate of 28% predicted from 2022-2032.
Types of audio or speech annotation
Audio annotation is used across several distinct industries for a variety of purposes. Audio annotation is an important part of developing virtual assistants and conversational AI that respond to human speech and voice commands. Virtual assistants like Amazon Alexa and Apple’s Siri, for example, need to know when they are being spoken to and issued a command as opposed to when they are simply picking up background voices in the environment that aren’t directed at them.
What types of audio annotation are there?
There are several types of audio annotation and each has distinct purposes and use cases. Some of the most popular types of audio annotation include the following:
Speech-to-text transcription
Transcription from speech to text is both a middle and end step for many different types of audio annotation. Sometimes transcription is all that is needed for a task, but sometimes transcription simply represents one stage in a larger process where transcribed speech is then used for other natural language processing tasks. Speech-to-text is often used in call center applications to make sense of the voices of their customers.
Utterances, intents & entities
Utterances, intents & entities are three categories that are commonly used in natural language processing to understand and process a command. An utterance is simply what is being said. A voice command, such as “show me the weather for tomorrow” is an utterance.
Intent is the meaning behind those words. There is a lot of ambiguity in language. Humans tend to deal with this naturally, using context and other clues to resolve ambiguities, often quickly and subconsciously. However, machines need to clearly understand the intent behind an utterance. Intent is usually defined using verbs and nouns. For example, in the command, “show me the weather for tomorrow”, the intent could be defined as ‘show weather’. This is a verb and noun that concisely defines the intent.
Entities are additional modifiers that can aid or alter the intent. In our example given above, ‘tomorrow’ is an entity. It specifies a time that does not change the overall intent but acts as a modifier as to which weather results to return. Location is another example of an entity, though in this case, one is not specified, so other contextual cues must be used to determine what location to pull weather forecasts from.
Speaker classification
Speaker classification involves determining whether a particular audio sample is human speech or not and identifying the properties of the voice used to produce sounds. This allows for multiple different voices to be individually distinguished in audio samples. This is useful for virtual assistants, who can be trained to only respond to particular voices rather than anyone who issues a comprehensible command.
Music classification
Music classification is a special type of audio classification. Its purpose is to pick up on specific aspects of a musical audio file. This can be used to identify the genre of a track or to identify and isolate instruments and vocals within a song. A common use for this type of technology is for the automatic categorization of music and building playlist recommendation engines based on personalized music preferences.
How do good data preparation practices impact audio annotation?
Good data preparation practices can make life much easier for audio annotation. Data preparation is a multi-step process that optimizes data for use with machine learning. In particular, data used for machine learning model training should be prepared with best practices in mind as the training data input is crucial to maximizing the success of the resulting model and its ability to generalize data that it has not been trained for.
Good data preparation practices as they relate to audio annotation include good data sourcing, cleansing, and annotation. Data sourcing refers to the practice of sourcing data for machine learning. Machine learning requires high volumes of high-quality data, which is not always easy to source. That’s why data cleansing is also important. This involves cleaning up data, filtering out anything unnecessary or irrelevant, and standardizing it such that all data follows a consistent format. Data annotation or labeling can also help as it enriches data, providing metadata that can help with the categorization, sorting, and storing of data.
The value of manual vs. AI-powered annotation
Manual annotation is the process of having humans listen to and annotate audio data. This is a time-consuming process, but it is important for the creation of high-quality training datasets. There are several ways in which organizations can arrange for audio to be manually annotated, though each has its own strengths and weaknesses. An organization may choose to deal with audio annotation tasks in-house, outsource them, or crowdsource them.
- In-house audio annotation. This means that you have full control over the whole process and can control for quality but it is costly.
- Outsourcing audio annotation. This is slightly cheaper but there is less control over the quality and there may be some concerns around data privacy regulations if personal or sensitive data is processed.
- Crowdsourcing audio annotation. This is the cheapest of all but also typically the lowest in quality and is unsuitable for dealing with data of a private or sensitive nature.
The main advantages of manual annotation are that is is more accurate and can be used to annotate a wide range of different types of data. However, the drawbacks compared to AI-powered annotation are that they are expensive, slow, and could be of variable quality. You also have to concern yourself with the rules and regulations of handling private data unless the company you outsource annotation work to, like Sigma.ai, has comprehensive security measures or secure facilities to deal with data security and privacy.
Get started with our audio annotation services
Audio annotation is fast becoming an important part of customer-oriented business practices.
Virtual assistants and other tools that rely on audio annotation can understand and respond to your customers’ requests even when they are phrased in a very natural and conversational manner. This means customers can simply describe their problem rather than having to follow specific procedures or press particular buttons. However, a lot of training data is needed, particularly for domain-specific knowledge, so that these machine learning models are capable of handling a wide variety of tasks related to responding to customers.
A study of the barriers to audio or voice technology annotation includes accuracy (73%), accent or dialect recognition issues (66%) and language coverage (38%).
Sigma guarantees 98% accuracy using our QA methodology and tools, and up to 99.99% upon request. We support over 250+ languages and dialects and rely on over 22,000+ highly trained annotators, linguists and subject matter experts in 5 continents, with access to a pool of over 900,000.
If you are looking to implement audio annotation as part of a project, contact us. Our team can help to guide you through the process so that you get the best results for your next audio annotation project.