Audio annotation services

Classification of sounds to train NLP models
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Audio annotation for AI

Audio annotation is a type of data labeling covering the classification of sounds - whether they are human, music, animal, or environmental. This data annotation type is essential for building accurate natural language processing (NLP) models for a wide range of speech-based solutions including automated speech recognition (ASR), chatbots, digital assistants and in-car systems.

With increasing customer expectations when it comes to the speed and
quality of customer service, including engagement with voice AI devices,
the quality of the data used to train Conversational AI has, in turn, become increasingly critical.


LXT for audio annotation

With LXT, you can quickly build a reliable data pipeline to power your Natural Language Processing (NLP) and Conversational AI solutions and focus your time on building the technologies of the future. The combination of our annotation platform, managed crowd, and quality methodologies delivers the high-quality data you need so you can build more accurate AI solutions and accelerate your time to market. Every client engagement is customized to fit the needs of your specific use case, and our quality guarantee ensures that our clients receive training data that meets or exceeds quality expectations.

Our audio annotation services include:


Acoustic noise annotation

Identify and label background sounds to improve speech recognition in noisy environments.

Natural language annotation (e.g. dialect labeling)

Annotate speech for semantics, dialects, sentiment and more.

Audio classification

This task involves analyzing audio recordings and assigning labels or classifications to them. Types of audio classification include acoustic, environmental, and music. This type of labeling helps in the development of virtual assistants in the recognition of speech from other types of audio.

Offensive language identification

Detect and remove potentially harmful messages from reviews, social media messages and more.

Event tracking and timestamping

Annotators place time stamps where certain events occur in the audio, for example a language or speaker change or a certain noise event. This will allow for the system to be trained to recognize different types of noise events that are likely to occur in a natural environment.

Speaker diarization

Identify distinct speakers in an audio file to transcribe call center, business meetings and other situations involving multiple speakers, to train Conversational AI solutions.

Linguistic annotation

Label audio files with metadata to make them understandable for machine learning models.

Speech-to-text transcription

This annotation type involves converting speech recordings into text and labeling the words and sounds in the recording. This data is then used to train speech recognition systems.

Multi-label non-speech audio annotation

This annotation method provides multiple labels in an audio file to help differentiate between overlapping audio sources.
Annotation & Enhancement - AI Data

Secure services

With the accelerating volumes of data created daily and the number of potential threats on the rise, security is an increasing area of concern for organizations across all industries. Our platform and processes are designed to ensure the security of your data.

To meet the most stringent security requirements, our facilities are
ISO 27001-certified and PCI DSS compliant. We also offer supervised transcription within a secure facility to safeguard your data. We will work closely with you to design a secure solution that meets your needs.


Reliable AI data at scale — guaranteed

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