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Thejaswi Muniyappa

As seen in: Amazon Science
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Articles

Contextual adapters for personalized speech recognition in neural transducers

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By Kanthashree Mysore Sathyendra, Thejaswi Muniyappa, Jing Liu, Jinru Su
| Amazon Science
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is with shallow fusion methods at inference time. However, due to their dependence on external language models and the deterministic approach to weight boosting, their performance is limited. In this paper, we propose training neural contextual adapters for personalization in neural transducer based ASR models.
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Dual-attention neural transducers for efficient wake word spotting in speech recognition

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By Saumya Sahai, Jing Liu, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra
| Amazon Science
WE ARE OPEN TO HIRING THIS ROLE IN SF BAY AREA, SEATTLE, TORONTO. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth.
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Dialog act guided contextual adapter for personalized speech recognition

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By Feng-Ju Chang, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra
| Amazon Science
Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog act, which is available in a multi-turn dialog scenario. In this work, we propose a dialog act guided contextual adapter network.
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