Search Results for author: Tara N. Sainath

Found 79 papers, 6 papers with code

Efficient Adapter Finetuning for Tail Languages in Streaming Multilingual ASR

no code implementations17 Jan 2024 Junwen Bai, Bo Li, Qiujia Li, Tara N. Sainath, Trevor Strohman

Meanwhile, the heterogeneous nature and imbalanced data abundance of different languages may cause performance degradation, leading to asynchronous peak performance for different languages during training, especially on tail ones.

Improved Long-Form Speech Recognition by Jointly Modeling the Primary and Non-primary Speakers

no code implementations18 Dec 2023 Guru Prakash Arumugam, Shuo-Yiin Chang, Tara N. Sainath, Rohit Prabhavalkar, Quan Wang, Shaan Bijwadia

ASR models often suffer from a long-form deletion problem where the model predicts sequential blanks instead of words when transcribing a lengthy audio (in the order of minutes or hours).

speech-recognition Speech Recognition

Text Injection for Capitalization and Turn-Taking Prediction in Speech Models

no code implementations14 Aug 2023 Shaan Bijwadia, Shuo-Yiin Chang, Weiran Wang, Zhong Meng, Hao Zhang, Tara N. Sainath

Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error rate.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Improving Joint Speech-Text Representations Without Alignment

no code implementations11 Aug 2023 Cal Peyser, Zhong Meng, Ke Hu, Rohit Prabhavalkar, Andrew Rosenberg, Tara N. Sainath, Michael Picheny, Kyunghyun Cho

The last year has seen astonishing progress in text-prompted image generation premised on the idea of a cross-modal representation space in which the text and image domains are represented jointly.

Speech Recognition

How to Estimate Model Transferability of Pre-Trained Speech Models?

1 code implementation1 Jun 2023 Zih-Ching Chen, Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Shuo-Yiin Chang, Rohit Prabhavalkar, Hung-Yi Lee, Tara N. Sainath

In this work, we introduce a "score-based assessment" framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks.

Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR

no code implementations28 May 2023 W. Ronny Huang, Hao Zhang, Shankar Kumar, Shuo-Yiin Chang, Tara N. Sainath

We address this limitation by distilling punctuation knowledge from a bidirectional teacher language model (LM) trained on written, punctuated text.

Language Modelling Semantic Segmentation +1

Mixture-of-Expert Conformer for Streaming Multilingual ASR

no code implementations25 May 2023 Ke Hu, Bo Li, Tara N. Sainath, Yu Zhang, Francoise Beaufays

We evaluate the proposed model on a set of 12 languages, and achieve an average 11. 9% relative improvement in WER over the baseline.

Automatic Speech Recognition speech-recognition +1

Practical Conformer: Optimizing size, speed and flops of Conformer for on-Device and cloud ASR

no code implementations31 Mar 2023 Rami Botros, Anmol Gulati, Tara N. Sainath, Krzysztof Choromanski, Ruoming Pang, Trevor Strohman, Weiran Wang, Jiahui Yu

Conformer models maintain a large number of internal states, the vast majority of which are associated with self-attention layers.

A Deliberation-based Joint Acoustic and Text Decoder

no code implementations23 Mar 2023 Sepand Mavandadi, Tara N. Sainath, Ke Hu, Zelin Wu

We propose a new two-pass E2E speech recognition model that improves ASR performance by training on a combination of paired data and unpaired text data.

speech-recognition Speech Recognition

End-to-End Speech Recognition: A Survey

no code implementations3 Mar 2023 Rohit Prabhavalkar, Takaaki Hori, Tara N. Sainath, Ralf Schlüter, Shinji Watanabe

In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, compared to modeling without deep learning.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

UML: A Universal Monolingual Output Layer for Multilingual ASR

no code implementations22 Feb 2023 Chao Zhang, Bo Li, Tara N. Sainath, Trevor Strohman, Shuo-Yiin Chang

Consequently, the UML enables to switch in the interpretation of each output node depending on the language of the input speech.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition

no code implementations16 Feb 2023 Zhong Meng, Weiran Wang, Rohit Prabhavalkar, Tara N. Sainath, Tongzhou Chen, Ehsan Variani, Yu Zhang, Bo Li, Andrew Rosenberg, Bhuvana Ramabhadran

We propose JEIT, a joint end-to-end (E2E) model and internal language model (ILM) training method to inject large-scale unpaired text into ILM during E2E training which improves rare-word speech recognition.

Language Modelling speech-recognition +1

From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition

no code implementations19 Jan 2023 Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Rohit Prabhavalkar, Tara N. Sainath, Trevor Strohman

In this work, we propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition, which can \textbf{re-purpose} well-trained English automatic speech recognition (ASR) models to recognize the other languages.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion

no code implementations4 Nov 2022 Zhouyuan Huo, Khe Chai Sim, Bo Li, Dongseong Hwang, Tara N. Sainath, Trevor Strohman

Experimental results show that the proposed method can achieve better performance on speech recognition task than existing algorithms with fewer number of trainable parameters, less computational memory cost and faster training speed.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

no code implementations2 Nov 2022 Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Tara N. Sainath, Sabato Marco Siniscalchi, Chin-Hui Lee

We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios.

Spoken Command Recognition

JOIST: A Joint Speech and Text Streaming Model For ASR

no code implementations13 Oct 2022 Tara N. Sainath, Rohit Prabhavalkar, Ankur Bapna, Yu Zhang, Zhouyuan Huo, Zhehuai Chen, Bo Li, Weiran Wang, Trevor Strohman

In addition, we explore JOIST using a streaming E2E model with an order of magnitude more data, which are also novelties compared to previous works.

Turn-Taking Prediction for Natural Conversational Speech

no code implementations29 Aug 2022 Shuo-Yiin Chang, Bo Li, Tara N. Sainath, Chao Zhang, Trevor Strohman, Qiao Liang, Yanzhang He

This makes doing speech recognition with conversational speech, including one with multiple queries, a challenging task.

speech-recognition Speech Recognition

Streaming Intended Query Detection using E2E Modeling for Continued Conversation

no code implementations29 Aug 2022 Shuo-Yiin Chang, Guru Prakash, Zelin Wu, Qiao Liang, Tara N. Sainath, Bo Li, Adam Stambler, Shyam Upadhyay, Manaal Faruqui, Trevor Strohman

In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query. However, speaking queries followed by a hotword each timeintroduces a cognitive burden in continued conversations.

Improving Deliberation by Text-Only and Semi-Supervised Training

no code implementations29 Jun 2022 Ke Hu, Tara N. Sainath, Yanzhang He, Rohit Prabhavalkar, Trevor Strohman, Sepand Mavandadi, Weiran Wang

Text-only and semi-supervised training based on audio-only data has gained popularity recently due to the wide availability of unlabeled text and speech data.

Language Modelling

Self-Supervised Speech Representation Learning: A Review

no code implementations21 May 2022 Abdelrahman Mohamed, Hung-Yi Lee, Lasse Borgholt, Jakob D. Havtorn, Joakim Edin, Christian Igel, Katrin Kirchhoff, Shang-Wen Li, Karen Livescu, Lars Maaløe, Tara N. Sainath, Shinji Watanabe

Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR

no code implementations22 Apr 2022 W. Ronny Huang, Shuo-Yiin Chang, David Rybach, Rohit Prabhavalkar, Tara N. Sainath, Cyril Allauzen, Cal Peyser, Zhiyun Lu

Improving the performance of end-to-end ASR models on long utterances ranging from minutes to hours in length is an ongoing challenge in speech recognition.

Sentence speech-recognition +1

Improving Rare Word Recognition with LM-aware MWER Training

no code implementations15 Apr 2022 Weiran Wang, Tongzhou Chen, Tara N. Sainath, Ehsan Variani, Rohit Prabhavalkar, Ronny Huang, Bhuvana Ramabhadran, Neeraj Gaur, Sepand Mavandadi, Cal Peyser, Trevor Strohman, Yanzhang He, David Rybach

Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the rescoring setups.

Streaming Align-Refine for Non-autoregressive Deliberation

no code implementations15 Apr 2022 Weiran Wang, Ke Hu, Tara N. Sainath

We propose a streaming non-autoregressive (non-AR) decoding algorithm to deliberate the hypothesis alignment of a streaming RNN-T model.

Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition

no code implementations9 Mar 2022 W. Ronny Huang, Cal Peyser, Tara N. Sainath, Ruoming Pang, Trevor Strohman, Shankar Kumar

We down-select a large corpus of web search queries by a factor of 53x and achieve better LM perplexities than without down-selection.

Language Modelling Sentence +2

Improving the fusion of acoustic and text representations in RNN-T

no code implementations25 Jan 2022 Chao Zhang, Bo Li, Zhiyun Lu, Tara N. Sainath, Shuo-Yiin Chang

The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Joint Unsupervised and Supervised Training for Multilingual ASR

no code implementations15 Nov 2021 Junwen Bai, Bo Li, Yu Zhang, Ankur Bapna, Nikhil Siddhartha, Khe Chai Sim, Tara N. Sainath

Our average WER of all languages outperforms average monolingual baseline by 33. 3%, and the state-of-the-art 2-stage XLSR by 32%.

Language Modelling Masked Language Modeling +3

Tied & Reduced RNN-T Decoder

no code implementations15 Sep 2021 Rami Botros, Tara N. Sainath, Robert David, Emmanuel Guzman, Wei Li, Yanzhang He

Previous works on the Recurrent Neural Network-Transducer (RNN-T) models have shown that, under some conditions, it is possible to simplify its prediction network with little or no loss in recognition accuracy (arXiv:2003. 07705 [eess. AS], [2], arXiv:2012. 06749 [cs. CL]).

Language Modelling

Scaling End-to-End Models for Large-Scale Multilingual ASR

no code implementations30 Apr 2021 Bo Li, Ruoming Pang, Tara N. Sainath, Anmol Gulati, Yu Zhang, James Qin, Parisa Haghani, W. Ronny Huang, Min Ma, Junwen Bai

Building ASR models across many languages is a challenging multi-task learning problem due to large variations and heavily unbalanced data.

Multi-Task Learning

Lookup-Table Recurrent Language Models for Long Tail Speech Recognition

no code implementations9 Apr 2021 W. Ronny Huang, Tara N. Sainath, Cal Peyser, Shankar Kumar, David Rybach, Trevor Strohman

We introduce Lookup-Table Language Models (LookupLM), a method for scaling up the size of RNN language models with only a constant increase in the floating point operations, by increasing the expressivity of the embedding table.

Language Modelling Sentence +2

Transformer Based Deliberation for Two-Pass Speech Recognition

no code implementations27 Jan 2021 Ke Hu, Ruoming Pang, Tara N. Sainath, Trevor Strohman

In this work, we explore using transformer layers instead of long-short term memory (LSTM) layers for deliberation rescoring.

speech-recognition Speech Recognition +1

Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging

no code implementations12 Dec 2020 Rohit Prabhavalkar, Yanzhang He, David Rybach, Sean Campbell, Arun Narayanan, Trevor Strohman, Tara N. Sainath

End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

A Better and Faster End-to-End Model for Streaming ASR

no code implementations21 Nov 2020 Bo Li, Anmol Gulati, Jiahui Yu, Tara N. Sainath, Chung-Cheng Chiu, Arun Narayanan, Shuo-Yiin Chang, Ruoming Pang, Yanzhang He, James Qin, Wei Han, Qiao Liang, Yu Zhang, Trevor Strohman, Yonghui Wu

To address this, we explore replacing the LSTM layers in the encoder of our E2E model with Conformer layers [4], which has shown good improvements for ASR.

Audio and Speech Processing Sound

FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization

1 code implementation21 Oct 2020 Jiahui Yu, Chung-Cheng Chiu, Bo Li, Shuo-Yiin Chang, Tara N. Sainath, Yanzhang He, Arun Narayanan, Wei Han, Anmol Gulati, Yonghui Wu, Ruoming Pang

FastEmit also improves streaming ASR accuracy from 4. 4%/8. 9% to 3. 1%/7. 5% WER, meanwhile reduces 90th percentile latency from 210 ms to only 30 ms on LibriSpeech.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling

no code implementations ICLR 2021 Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu, Bo Li, Tara N. Sainath, Yonghui Wu, Ruoming Pang

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible, while full-context ASR waits for the completion of a full speech utterance before emitting completed hypotheses.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus

no code implementations24 Aug 2020 Cal Peyser, Sepand Mavandadi, Tara N. Sainath, James Apfel, Ruoming Pang, Shankar Kumar

End-to-end (E2E) automatic speech recognition (ASR) systems lack the distinct language model (LM) component that characterizes traditional speech systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion

no code implementations19 May 2020 Cal Peyser, Tara N. Sainath, Golan Pundak

Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pronunciation similar to that of a more common word.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions

no code implementations7 May 2020 Chung-Cheng Chiu, Arun Narayanan, Wei Han, Rohit Prabhavalkar, Yu Zhang, Navdeep Jaitly, Ruoming Pang, Tara N. Sainath, Patrick Nguyen, Liangliang Cao, Yonghui Wu

On a long-form YouTube test set, when the nonstreaming RNN-T model is trained with shorter segments of data, the proposed combination improves word error rate (WER) from 22. 3% to 14. 8%; when the streaming RNN-T model trained on short Search queries, the proposed techniques improve WER on the YouTube set from 67. 0% to 25. 3%.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Towards Fast and Accurate Streaming End-to-End ASR

no code implementations24 Apr 2020 Bo Li, Shuo-Yiin Chang, Tara N. Sainath, Ruoming Pang, Yanzhang He, Trevor Strohman, Yonghui Wu

RNN-T EP+LAS, together with MWER training brings in 18. 7% relative WER reduction and 160ms 90-percentile latency reductions compared to the original proposed RNN-T EP model.

Audio and Speech Processing

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

no code implementations28 Mar 2020 Tara N. Sainath, Yanzhang He, Bo Li, Arun Narayanan, Ruoming Pang, Antoine Bruguier, Shuo-Yiin Chang, Wei Li, Raziel Alvarez, Zhifeng Chen, Chung-Cheng Chiu, David Garcia, Alex Gruenstein, Ke Hu, Minho Jin, Anjuli Kannan, Qiao Liang, Ian McGraw, Cal Peyser, Rohit Prabhavalkar, Golan Pundak, David Rybach, Yuan Shangguan, Yash Sheth, Trevor Strohman, Mirko Visontai, Yonghui Wu, Yu Zhang, Ding Zhao

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.

Sentence

Deliberation Model Based Two-Pass End-to-End Speech Recognition

no code implementations17 Mar 2020 Ke Hu, Tara N. Sainath, Ruoming Pang, Rohit Prabhavalkar

End-to-end (E2E) models have made rapid progress in automatic speech recognition (ASR) and perform competitively relative to conventional models.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Recognizing long-form speech using streaming end-to-end models

no code implementations24 Oct 2019 Arun Narayanan, Rohit Prabhavalkar, Chung-Cheng Chiu, David Rybach, Tara N. Sainath, Trevor Strohman

In this work, we examine the ability of E2E models to generalize to unseen domains, where we find that models trained on short utterances fail to generalize to long-form speech.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Improving Performance of End-to-End ASR on Numeric Sequences

no code implementations1 Jul 2019 Cal Peyser, Hao Zhang, Tara N. Sainath, Zelin Wu

This out-of-vocabulary (OOV) issue is addressed in conventional ASR systems by training part of the model on spoken domain utterances (e. g.

speech-recognition Speech Recognition

Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models

no code implementations21 Jun 2019 Ke Hu, Antoine Bruguier, Tara N. Sainath, Rohit Prabhavalkar, Golan Pundak

Contextual automatic speech recognition, i. e., biasing recognition towards a given context (e. g. user's playlists, or contacts), is challenging in end-to-end (E2E) models.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

A spelling correction model for end-to-end speech recognition

no code implementations19 Feb 2019 Jinxi Guo, Tara N. Sainath, Ron J. Weiss

Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs.

Language Modelling speech-recognition +2

Contextual Speech Recognition with Difficult Negative Training Examples

no code implementations29 Oct 2018 Uri Alon, Golan Pundak, Tara N. Sainath

Improving the representation of contextual information is key to unlocking the potential of end-to-end (E2E) automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Deep context: end-to-end contextual speech recognition

no code implementations7 Aug 2018 Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar, Anjuli Kannan, Ding Zhao

Our approach, which we re- fer to as Contextual Listen, Attend and Spell (CLAS) jointly- optimizes the ASR components along with embeddings of the context n-grams.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models

no code implementations5 Dec 2017 Tara N. Sainath, Rohit Prabhavalkar, Shankar Kumar, Seungji Lee, Anjuli Kannan, David Rybach, Vlad Schogol, Patrick Nguyen, Bo Li, Yonghui Wu, Zhifeng Chen, Chung-Cheng Chiu

However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units.

Language Modelling

Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models

2 code implementations5 Dec 2017 Rohit Prabhavalkar, Tara N. Sainath, Yonghui Wu, Patrick Nguyen, Zhifeng Chen, Chung-Cheng Chiu, Anjuli Kannan

Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds to improving the log-likelihood of the data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

State-of-the-art Speech Recognition With Sequence-to-Sequence Models

4 code implementations5 Dec 2017 Chung-Cheng Chiu, Tara N. Sainath, Yonghui Wu, Rohit Prabhavalkar, Patrick Nguyen, Zhifeng Chen, Anjuli Kannan, Ron J. Weiss, Kanishka Rao, Ekaterina Gonina, Navdeep Jaitly, Bo Li, Jan Chorowski, Michiel Bacchiani

Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Improving the Performance of Online Neural Transducer Models

no code implementations5 Dec 2017 Tara N. Sainath, Chung-Cheng Chiu, Rohit Prabhavalkar, Anjuli Kannan, Yonghui Wu, Patrick Nguyen, Zhifeng Chen

Neural transducer is a streaming sequence-to-sequence model, but has shown a significant degradation in performance compared to non-streaming models such as Listen, Attend and Spell (LAS).

Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model

no code implementations5 Dec 2017 Bo Li, Tara N. Sainath, Khe Chai Sim, Michiel Bacchiani, Eugene Weinstein, Patrick Nguyen, Zhifeng Chen, Yonghui Wu, Kanishka Rao

Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network.

speech-recognition Speech Recognition

Multilingual Speech Recognition With A Single End-To-End Model

no code implementations6 Nov 2017 Shubham Toshniwal, Tara N. Sainath, Ron J. Weiss, Bo Li, Pedro Moreno, Eugene Weinstein, Kanishka Rao

Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Learning Compact Recurrent Neural Networks

no code implementations9 Apr 2016 Zhiyun Lu, Vikas Sindhwani, Tara N. Sainath

Recurrent neural networks (RNNs), including long short-term memory (LSTM) RNNs, have produced state-of-the-art results on a variety of speech recognition tasks.

speech-recognition Speech Recognition

Structured Transforms for Small-Footprint Deep Learning

no code implementations NeurIPS 2015 Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar

We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile devices.

Keyword Spotting speech-recognition +1

Improvements to deep convolutional neural networks for LVCSR

no code implementations5 Sep 2013 Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E. Dahl, George Saon, Hagen Soltau, Tomas Beran, Aleksandr Y. Aravkin, Bhuvana Ramabhadran

We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline.

Speech Recognition

Accelerating Hessian-free optimization for deep neural networks by implicit preconditioning and sampling

no code implementations5 Sep 2013 Tara N. Sainath, Lior Horesh, Brian Kingsbury, Aleksandr Y. Aravkin, Bhuvana Ramabhadran

This study aims at speeding up Hessian-free training, both by means of decreasing the amount of data used for training, as well as through reduction of the number of Krylov subspace solver iterations used for implicit estimation of the Hessian.

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