State-of-the-art speech recognition systems find it challenging to distinguish between homophones and proper names and separate words, in addition to identifying domain-specific words accurately. Moreover, standalone speech recognition software cannot produce accurate data from the enormous amount of information captured by enterprises from spoken conversations for reliable Natural Language Processing (NLP).
The key, therefore, to understanding spoken conversations via speech recognition systems is domain-specific taxonomies. With them, conversations can be fed to knowledge graphs, which can extract the correct meaning of the text from conversations and connect concepts to add business value.
The merit of these business value applications of speech recognition and text analytics stems from the taxonomy foundation that is core to knowledge graphs. Clarifying the relevant words, their meaning, synonyms, and hierarchies of meaning for a specific domain deliver maximum organizational utility for speech recognition capabilities. Such taxonomies deliver the best results for speech recognizers deriving text from spoken words and are essential for extracting entities on marketing, sales, call types, competitor references, and everything else. By coupling these capabilities in knowledge graphs, enterprises get the additional benefit of readily traversing them for everything relevant to specific use cases— from issuing recommendations to collateral material to competitor shortfalls and even successful previous agent approaches with customer segments. These repositories are the most effective means of democratizing this knowledge and applying it outside of speech recognition to any text analytics.
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