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Gathered Multilingual Audio Data
to enhance voice-enabled software applications
Recordings
0
K+
Accuracy
0
%
About the Customer
Founded in 2015, this Seattle-based client with an R&D centre in Lisbon, Portugal is a smart data curation platform for Artificial Intelligence (AI) and Machine Learning (ML). They offer efficient data workflows to collect, process and enrich training data by combining crowdsourcing, tools, and machine learning capabilities to accelerate enterprise machine learning training and modelling. With strong expertise in speech and natural language processing technologies, the client’s platform has been serving top AI companies and Fortune 500 companies since their inception.
Problem
Training AI algorithms demand high-quality labelled and validated data, which is why crafting the training data can take nearly as long as developing the models that ingest them. Speech to text conversion or compilation and validation is a complex process as it includes various phases such as feature extraction, audio sampling and speech recognition to go through various tonalities and sounds to convert the language and speech into text. However, understanding and representing the human language itself is a difficult process as it is discrete and leads to ambiguity due to the complexity in representing and interpreting the language.
Solution
Recent advances in Natural Language Processing (NLP) are encouraging domains to create data-driven models to eradicate loopholes of traditional technologies to improve the outdated dialogue tracking. NextWealth’s NLP capabilities were used to crowdsource, train and deploy data sets for multi-language speech recognition such as speech tagging, sentiment analysis, and general understanding of the speech. This artificial intelligence-based language programming, programs human traits and helps machines and systems process human structure sentences effectively.
Results
Crowdsourcing helped gather multi-lingual audio data to enhance voice-enabled software applications. End Customers could now be trained on how to use the platform effectively and build speech models with accurate audio collection and annotation, while fully customizing desired domain/intent, demographic distribution, and recording device type.