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Automatic Speech Recognition (ASR) plays a crucial role in voice-basedapplications. For applications requiring real-time feedback like Voice Search,streaming capability becomes vital. While LSTM/RNN and CTC based ASR systemsare commonly employed for low-latency streaming applications, they oftenexhibit lower accuracy compared to state-of-the-art models due to a lack offuture audio frames. In this work, we focus on developing accurate LSTM,attention, and CTC based streaming ASR models for large-scale Hinglish (a blendof Hindi and English) Voice Search. We investigate various modifications invanilla LSTM training which enhance the system's accuracy while preserving itsstreaming capabilities. We also address the critical requirement ofend-of-speech (EOS) detection in streaming applications. We present a simpletraining and inference strategy for end-to-end CTC models that enables jointASR 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. Ourmodel achieves a word error rate (WER) of 3.69% without EOS and 4.78% with EOSwhile also reducing the search latency by approximately ~1300 ms (equivalent to46.64% reduction) when compared to an independent voice activity detection(VAD) model.

Building Accurate Low Latency ASR for Streaming Voice Search