End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English

It helps us reduce the requirement of manually annotated training data. Spoken Language Understanding (SLU) systems In this work, we adapt an E2E ASR model to that extract the intent from a spoken utterance are build an E2E S2I model for Flipkart's on-call customer integral in various voicebot applications such as support. An overview of our contributions is automated on-call customer support, voice assistants, as follows: home or vehicle automation systems, etc. The extracted intent triggers a standard operating An efficient extension of end-to-end BiLSTM procedure (SOP) as defined by the respective application, and CTC based ASR models for S2I task on e.g. an e-commerce customer query "I want noisy datasets; to return my phone" maps to "Return" intent which A demonstration of how the idea can outperform triggers the SOP to help the user with returns. It conventional pipeline in customer support helps us reduce the reliance on human agents and voicebot in real-world settings; provide faster resolutions. More elaborate examples are shown in Table 4. An investigation on how ASR pre-training, Conventionally, such systems consist of two offline active learning and pseudo labelling components - an Automatic Speech Recognition reduce data labeling requirements for S2I.