  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.
