Our latest Unified version 4.x models are the solution to both transcription accuracy and latency. Therefore, it's important to be able to customize the model to your locale. WER is highly correlated with how well the acoustic and language models are trained for a specific locale. One of the key challenges in call center transcription is the noise that’s prevalent in the call center (for example, other agents speaking in the background), the rich variety of language locales and dialects, and the low quality of the actual telephone signal. Because many of the downstream analytics processes rely on transcribed text, the word error rate (WER) metric is of utmost importance. Speech-to-text is the most sought-after feature in any call center solution. Whether the domain is post-call or real-time, Azure offers a set of mature and emerging technologies to help improve the customer experience. Here is an architecture diagram showing a typical implementation of a batch scenario:Ĭomponents of speech analytics technology Voice assistants (bots), which either drive the dialogue between customers and the bot in an attempt to solve their issues, without agent participation, or apply AI protocols to assist the agent. ![]()
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