Automatic Speech Recognition (ASR) plays a crucial role in voice-based applications. For applications requiring real-time feedback like Voice Search, streaming capability becomes vital. While LSTM/RNN and CTC based ASR systems are commonly employed for low-latency streaming applications, they often exhibit lower accuracy compared to state-of-the-art models due to a lack of future audio frames. In this work, we focus on developing accurate LSTM, attention, and CTC based streaming ASR models for large-scale Hinglish (a blend of Hindi and English) Voice Search. We investigate various modifications in vanilla LSTM training which enhance the system's accuracy while preserving its streaming capabilities. We also address the critical requirement of end-of-speech (EOS) detection in streaming applications. We present a simple training and inference strategy for end-to-end CTC models that enables joint ASR and EOS detection. The evaluation of our model on Flipkart's Voice Search, which handles substantial traffic of approximately 6 million queries per day, demonstrates significant performance gains over the vanilla LSTM-CTC model. Our model achieves a word error rate (WER) of 3.69% without EOS and 4.78% with EOS while also reducing the search latency by approximately ~1300 ms (equivalent to 46.64% reduction) when compared to an independent voice activity detection (VAD) model.

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.