Výsledky vyhledávání - "Gautam Tiwari"

On joint training with interfaces for spoken language understanding

Autor: Anirudh Raju, Milind Rao, Gautam Tiwari, PRANAV DHERAM, Bryan Anderson, Zhe Zhang, Chul Lee, Bach Bui, Ariya Rastrow

Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances. SLU systems usually consist of (1) an automatic speech recognition (ASR) module, (2) an interface

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7bfaa2873069dca4e45bac40bf733f7
http://arxiv.org/abs/2106.15919

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Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding

Autor: Gautam Tiwari, Milind Rao, Ariya Rastrow, Pranav Dheram, Jasha Droppo, Anirudh Raju, Andreas Stolcke

Publikováno v: ICASSP

Spoken language understanding (SLU) systems extract transcriptions, as well as semantics of intent or named entities from speech, and are essential components of voice activated systems. SLU models, which either directly extract semantics from audio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5af2027a11a46339cab8f36cd1ce317e

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Personalization Strategies for End-to-End Speech Recognition Systems

Autor: Yile Gu, Gautam Tiwari, Ivan Bulyko, Shashank Kalmane, Linda Liu, Andreas Stolcke, Guitang Lan, Xiangyang Huang, Denis Filimonov, Ankur Gandhe, Ariya Rastrow, Aditya Gourav

Publikováno v: ICASSP

The recognition of personalized content, such as contact names, remains a challenging problem for end-to-end speech recognition systems. In this work, we demonstrate how first and second-pass rescoring strategies can be leveraged together to improve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b478fe456c395d84b6ae8a16f4a1ab7a

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Efficient Minimum Word Error Rate Training of RNN-Transducer for End-to-End Speech Recognition

Autor: Che-Wei Huang, Andreas Stolcke, Maarten Van Segbroeck, Gautam Tiwari, Jinxi Guo, Jasha Droppo, Roland Maas

Publikováno v: INTERSPEECH

In this work, we propose a novel and efficient minimum word error rate (MWER) training method for RNN-Transducer (RNN-T). Unlike previous work on this topic, which performs on-the-fly limited-size beam-search decoding and generates alignment scores f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1006ea04f87a85ca66e40f206119ecd
https://doi.org/10.21437/interspeech.2020-1557

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Scalable Multi Corpora Neural Language Models for ASR

Autor: Denis Filimonov, Gautam Tiwari, Ariya Rastrow, Anirudh Raju, Guitang Lan

Publikováno v: INTERSPEECH

Neural language models (NLM) have been shown to outperform conventional n-gram language models by a substantial margin in Automatic Speech Recognition (ASR) and other tasks. There are, however, a number of challenges that need to be addressed for an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::523fe586ce2d253737171971cdc5d27d
http://arxiv.org/abs/1907.01677

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Combining Acoustic Embeddings and Decoding Features for End-of-Utterance Detection in Real-Time Far-Field Speech Recognition Systems

Autor: Chengyuan Ma, Bjorn Hoffmeister, Roland Maas, Guitang Lan, Gautam Tiwari, Shaun N. Joseph, Kyle Goehner, Ariya Rastrow

Publikováno v: ICASSP

We present an end-of-utterance detector for real-time automatic speech recognition in far-field scenarios. The proposed system consists of three components: a long short-term memory (LSTM) neural network trained on acoustic features, an LSTM trained

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b7a0cfbcd2c7bd57c765872d21923300
https://doi.org/10.1109/icassp.2018.8461478

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