mlsanityData to 14 Oct 2026

Attention based end to end Speech Recognition for Voice Search in Hindi and English

arXiv 2111.10208 ↗First posted 15 Nov 2021eess.AS cs.IR cs.LGcited by 4

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Abstract (arXiv)

We describe here our work with automatic speech recognition (ASR) in the context of voice search functionality on the Flipkart e-Commerce platform. Starting with the deep learning architecture of Listen-Attend-Spell (LAS), we build upon and expand the model design and attention mechanisms to incorporate innovative approaches including multi-objective training, multi-pass training, and external rescoring using language models and phoneme based losses. We report a relative WER improvement of 15.7% on top of state-of-the-art LAS models using these modifications. Overall, we report an improvement of 36.9% over the phoneme-CTC system. The paper also provides an overview of different components that can be tuned in a LAS-based system.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

20 of 43 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2022A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Datacited by 7
  2. 2022On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Modecited by 2
  3. 2023Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learningcited by 2
  4. 2023Code-Mixed Text to Speech Synthesis under Low-Resource Constraintscited by 0