SPEECH AND TEXT ANNOTATION
SPEECH AND TEXT ANNOTATION
Leaders in Data Annotation for Natural Language
Natural language is complex and full of nuance. Our decades-long experience in speech and text annotation, along with our coverage of over 300+ languages and dialects, means we provide your AI with the training data it needs to interpret all aspects of language — from basic mechanics to context, dialogue, sentiment and emotion.
Data Annotation Services for Speech and Text
With the support of our customizable annotation tooling, we offer extensive services for interpreting and processing text and speech. Don’t see a service you need, or unsure where to start? Contact us for more information.
Transcription and Diarization
We convert recorded speech of all types and languages into text. Whether you need verbatim transcripts or transcripts cleaned of filler words and noise, added speaker or noise identification with timestamps (diarization), or even phonetic transcriptions, we provide you best-in-class transcriptions the way you want them, when you need them.
Text annotation converts a text into a dataset to use in Natural Language Processing. To interpret texts, we break them down and structure them by key elements, or entities. This establishes a basis for annotators to label elements by category such as places, dates, brands or prices, delineate relationships between words and phrases, and apply many other labeling methods.
Annotators read a written input or listen to a voice recording and classify it according to what the speaker or writer wants to achieve. Intent recognition is useful for call centers, chatbots and intelligent agents since it provides relevant information about a user’s needs and requests.
Data relevance, or search relevance, assesses whether a system like a search engine or intelligent assistant gives a response to a user that matches what the user requested. Annotators check whether the response is relevant to the user request, and the request itself to see whether the inputs are unclear or unexpected.
Sentiment and Emotion Analysis
Sentiment and emotion analysis seeks to understand the human context behind a text or speech recording. Sentiment analysis determines whether a segment of speech or text is positive, negative or neutral and can help gauge customer opinion or brand reputation. Emotion annotation provides deeper insight into whether a speaker is feeling anger, happiness, sadness, fear or surprise.
Pronunciation and Dialect Assessment
Annotators can determine whether the the pronunciation of a word or sentence is correct, based on standard pronunciation or dialect variants. They can also identify various dialects within a spoken language. Pronunciation and dialect assessment can be performed on human or synthetic speech.
Conversational AI Annotation
Conversational AI combines natural language processing (NLP) and machine learning to allow applications to speak and respond in a human-like way — for example chatbots or voice assistants. For training chatbots, annotators carry out natural conversations as if they were an agent or customer and role play entire scenarios with the AI, from greeting to sign-off. For voice assistants, annotators listen to collected audio data and validate, categorize, transcribe or correct machine-generated pretranscriptions of conversations. In both cases, they rate the conversational quality based on helpfulness or other client criteria.
Translation and Localization
While translation involves re-writing a given message from one language to another, localization incorporates relevant cultural context and language connotation to adapt the full meaning of a message for a target region or cultural group. This makes the message more appealing and familiar to local readers. Annotators can measure the estimated accuracy of a predictive translation or localization against a human-generated translation, and can also identify any possible mistranslations that might result from automated translation.
Our annotators can screen, monitor and filter inappropriate user-generated content, such as abusive, fake, explicit or harmful data, following specific client guidelines and platform requirements. They can categorize data that, for example, contains or suggests self-harm, violence, abuse, or drug references, and remove these specific media.
Natural language processing (NLP) is the process which allows a computer program to understand language as it is normally spoken and written. The use of machine learning models in NLP enables computers to better understand human language.
A major technology services client needs 2000 hours of video in 24 languages transcribed by humans — and wants to launch all 24 teams at once. Sigma.AI delivers.
Audio annotation is about adding metadata like tags, descriptions or labels to identify what is happening in an audio file. It’s the foundation for building the models used to analyze spoken words, speed up customer responses, or recognize spoken human emotions.
Let’s Work Together to Build Smarter AI
Whether you need help sourcing and annotating training data at scale, or you need a full-fledged annotation strategy to serve your AI training needs, we can help. Get in touch for more information or to set up your proof-of-concept.