Hello everyone. I am currently tuning our new “Customer Frustration” topic detection model. I am noticing a significant discrepancy. The topic fires accurately when customers call in using high-quality mobile or VoIP connections. However, when customers call from legacy landlines or areas with poor cellular reception, the transcription engine generates “Word Salad”, and the topic completely fails to trigger, even when the human reviewer clearly hears the customer yelling. How can we configure the speech analytics engine to be more forgiving or to use “Phonetic Matching” instead of strict dictionary-based transcription for these low-quality audio recordings?
I evaluate vendors for a living, and this is a common challenge with purely LVCSR (Large Vocabulary Continuous Speech Recognition) engines. Genesys Cloud relies heavily on its transcript accuracy. If the audio quality is too poor to generate a valid transcript, the text-based topic detection will inherently fail. You cannot switch Genesys Cloud to a raw phonetic engine. However, you can mitigate this by building “Acoustic” rules into your topics. Instead of just looking for words like “Angry”, configure your topic to also trigger on acoustic indicators like sudden spikes in volume or cross-talk (where the customer is speaking over the agent).
I develop API integrations for outbound dialers. I have seen this issue a lot with our predictive campaigns. You can use the Analytics API to pull the mediaStats for the interaction before you even run the speech analytics. If you see a high percentage of packet loss or low MOS score, you can flag the call programmatically and exclude it from your automated QA scoring, routing it to a human instead.
I manage dialer operations. is spot on. Do not let poor audio skew your AI analytics. Filter them out beforehand using the API.