… Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E)
models for automatic speech recognition. Currently, there are three promising E2E methods: …

… We design an online end-to-end speech recognition system based on Time-Depth Separable
(TDS) convolutions and Connectionist Temporal Classification (CTC). We improve the …

… Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech recognition …

… This section describes several decoding algorithms for endto-end speech recognition.
The basic decoding algorithm of endto-end ASR tries to estimate the most likely sequence ˆC …

… investigated the end-to-end speech recognition system for … a Tamil model using DeepSpeech.
Different challenges of low-… ., “Deep speech 2: End-to-end speech recognition in English …

… Deep Speech model ie, deepspeech-0.9.3 to develop an end-to-end speech recognition …
This paper will present the complete process of fine tuning the Deep Speech system for Indian-…

… to children’s speech. Notably, this includes the rapidly advancing neural network based
end-to-end speech recognition systems. Children speech recognition is more challenging due to …

… In this paper, we review various end-to-end automatic speech recognition algorithms and
their optimization techniques for on-device applications. Conventional speech recognition …

… In this work, we have presented several strategies to improve personal content recognition
for end-to-end speech recognition systems. We have outlined a novel algorithm for efficient …

… Transformer-based ASR solutions were found applicable in end-to-end systems and also
turned out to be better than many other modern ASR solutions. The Transformer architecture …