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Conversational AI / Natural-language processing

A quick guide to Amazon’s 40-plus papers at Interspeech 2022

Speech recognition and text-to-speech predominate, but other topics include audio watermarking, automatic dubbing, and compression.

By Staff writer
September 06, 2022
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Of Amazon’s more than 40 papers at this year’s Interspeech, automatic speech recognition and text-to-speech account for about half. But the others cover a range of topics, from acoustic watermarking and automatic dubbing to quantization and fairness.

Acoustic watermarking

Practical over-the-air perceptual acoustic watermarking
Ameya Agaskar

Audio classification

CNN-based audio event recognition for automated violence classification and rating for Prime Video content
Tarun Gupta, Mayank Sharma, Kenny Qiu, Xiang Hao, Raffay Hamid

Impact of acoustic event tagging on scene classification in a multi-task learning framework
Rahil Parikh, Harshavardhan Sundar, Ming Sun, Chao Wang, Spyros Matsoukas

Automatic dubbing

Isochrony-aware neural machine translation for automatic dubbing
Derek Tam, Surafel Melaku Lakew, Yogesh Virkar, Prashant Mathur, Marcello Federico

Prosodic alignment for off-screen automatic dubbing
Yogesh Virkar, Marcello Federico, Robert Enyedi, Roberto Barra-Chicote

Automatic speech recognition

Compute cost amortized transformer for streaming ASR
Yi Xie, Jonathan Macoskey, Martin Radfar, Feng-Ju Chang, Brian King, Ariya Rastrow, Athanasios Mouchtaris, Grant Strimel

Cost amortized Transformer.png
"Compute cost amortized Transformer for streaming ASR" proposes a mechanism that toggles components of Transformer blocks on and off to use computational resources more efficiently.

Content-context factorized representations for automated speech recognition
David M. Chan, Shalini Ghosh

ConvRNN-T: Convolutional augmented recurrent neural network transducers for streaming speech recognition
Martin Radfar, Rohit Barnwal, Rupak Vignesh Swaminathan, Feng-Ju Chang, Grant Strimel, Nathan Susanj, Athanasios Mouchtaris

Directed speech separation for automatic speech recognition of long-form conversational speech
Rohit Paturi, Sundararajan Srinivasan, Katrin Kirchhoff, Daniel Garcia-Romero

Domain prompts: Towards memory and compute efficient domain adaptation of ASR systems
Saket Dingliwa, Ashish Shenoy, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, Katrin Kirchhoff

Incremental learning for RNN-Transducer based speech recognition models
Deepak Baby, Pasquale D'Alterio, Valentin Mendelev

Knowledge distillation via module replacing for automatic speech recognition with recurrent neural network transducer
Kaiqi Zhao, Hieu Duy Nguyen, Animesh Jain, Nathan Susanj, Athanasios Mouchtaris, Lokesh Gupta, Ming Zhao

Learning to rank with BERT-based confidence models in ASR rescoring
Ting-Wei Wu, I-FAN CHEN, Ankur Gandhe

Reducing geographic disparities in automatic speech recognition via elastic weight consolidation
Viet Anh Trinh, Pegah Ghahremani, Brian King, Jasha Droppo, Andreas Stolcke, Roland Maas

RefTextLAS: Reference text biased listen, attend, and spell model for accurate reading evaluation
Phani Sankar Nidadavolu, Na Xu, Nick Jutila, Ravi Teja Gadde, Aswarth Abhilash Dara, Joseph Savold, Sapan Patel, Aaron Hoff, Veerdhawal Pande, Kevin Crews, Ankur Gandhe, Ariya Rastrow, Roland Maas

RNN-T lattice enhancement by grafting of pruned paths
Mirek Novak, Pavlos Papadopoulos

Using data augmentation and consistency regularization to improve semi-supervised speech recognition
Ashtosh Sapru

Dialogue

Contextual acoustic barge in classification for spoken dialog systems
Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati, Srikanth Ronanki, Katrin Kirchhoff

Adversarial reweighting.png
The method presented in "Adversarial reweighting for speaker verification fairness" uses an adversarial network to identify underperforming groups in a speaker verification dataset (green) and adjusts their contribution to the training loss (bottom).

Fairness

Toward fairness in speech recognition: Discovery and mitigation of performance disparities
Pranav Dheram, Murugesan Ramakrishnan, Anirudh Raju, I-Fan Chen, Brian King, Katherine Powell, Melissa Saboowala, Karan Shetty, Andreas Stolcke

Keyword spotting

Latency control for keyword spotting
Christin Jose, Joe Wang, Grant Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis

Language identification

A multimodal strategy for singing language identification
Wo Jae Lee, Emanuele Coviello

Multidevice processing

Challenges and opportunities in multi-device speech processing
Gregory Ciccarelli, Jarred Barber, Arun Nair, Israel Cohen, Tao Zhang

Multiparty speech

Separator-transducer-segmenter: Streaming recognition and segmentation of multi-party speech
Ilya Sklyar, Anna Piunova, Christian Osendorfer

Natural-language understanding

Phonetic embedding for ASR robustness in entity resolution
Xiaozhou Zhou, Ruying Bao, William M. Campbell

Quantization

Squashed weight distribution for low bit quantization of deep models
Nikko Ström, Haidar Khan, Wael Hamza

Sub-8-bit quantization aware training for 8-bit neural network accelerator with on device speech recognition
Kai Zhen, Hieu Duy Nguyen, Raviteja Chinta, Nathan Susanj, Athanasios Mouchtaris, Tariq Afzal, Ariya Rastrow

Sub-8-bit quantization.png
The training behavior of the algorithm proposed in "Sub-8-bit quantization aware training for 8-bit neural network accelerator with on device speech recognition", in which weights are optimized to lower quantization loss.

Signal processing

Clock skew robust acoustic echo cancellation
Karim Helwani, Erfan Soltanmohammadi, Michael M. Goodwin, Arvindh Krishnaswamy

Real-time packet loss concealment with mixed generative and predictive model
Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery, Timothy B. Terriberry, Michael Klingbeil, Paris Smaragdis, Arvindh Krishnaswamy

Speaker identification/verification

Adversarial reweighting for speaker verification fairness
Minho Jin, Chelsea J.-T. Ju, Zeya Chen, Yi Chieh Liu, Jasha Droppo, Andreas Stolcke

Graph-based multi-view fusion and local adaptation: Mitigating within household confusability for speaker identification
Long Chen, Yixiong Meng, Venkatesh Ravichandran, Andreas Stolcke

Graph fusion and fairness.png
The method proposed in "Graph-based multi-view fusion and local adaptation" propagates labels across a graph whose nodes represent utterances and whose weighted edges quantify the similarity between utterances.

Spoken-language understanding

Learning under label noise for robust spoken language understanding systems
Anoop Kumar, Pankaj Sharma, Aravind Illa, Sriram Venkatapathy, Subhrangshu Nandi, Pritam Varma, Anurag Dwarakanath, Aram Galstyan

On joint training with interfaces for spoken language understanding
Anirudh Raju, Milind Rao, Gautam Tiwari, Pranav Dheram, Bryan Anderson, Zhe Zhang, Chul Lee, Bach Bui, Ariya Rastrow

Text-to-speech

Automatic evaluation of speaker similarity
Kamil Deja, Ariadna Sanchez, Julian Roth, Marius Cotescu

CopyCat2: A single model for multi-speaker TTS and many-to-many fine-grained prosody transfer
Sri Karlapati, Penny Karanasou, Mateusz Lajszczak, Ammar Abbas, Alexis Moinet, Peter Makarov, Ray Li, Arent van Korlaar, Simon Slangen, Thomas Drugman

Creating new voices.png
Voices created through the method presented in "Creating new voices using normalizing flows" (green) are spread across the embedding space of voices from the training set (blue), confirming that the method can generate a variety of new voices.

Creating new voices using normalizing flows
Piotr Biliński, Tom Merritt, Abdelhamid Ezzerg, Kamil Pokora, Sebastian Cygert, Kayoko Yanagisawa, Roberto Barra-Chicote, Daniel Korzekwa

Cross-lingual style transfer with conditional prior VAE and style loss
Dino Ratcliffe, You Wang, Alex Mansbridge, Penny Karanasou, Alexis Moinet, Marius Cotescu

End-to-end LPCNet: A neural vocoder with fully-differentiable LPC estimation
Krishna Subramani, Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy

Expressive, variable, and controllable duration modelling in TTS
Ammar Abbas, Tom Merritt, Alexis Moinet, Sri Karlapati, Ewa Muszynska, Simon Slangen, Elia Gatti, Thomas Drugman

GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion
Magdalena Proszewska, Grzegorz Beringer, Daniel Saez Trigueros, Tom Merritt, Abdelhamid Ezzerg, Roberto Barra-Chicote

L2-GEN: A neural phoneme paraphrasing approach to L2 speech synthesis for mispronunciation diagnosis
Daniel Zhang, Ashwinkumar Ganesan, Sarah Campbell, Daniel Korzekwa

Low data? No problem: low resource, language-agnostic conversational text-to-speech via F0- conditioned data augmentation
Giulia Comini, Goeric Huybrechts, Manuel Sam Ribeiro, Adam Gabrys, Jaime Lorenzo Trueba

Mix and match: An empirical study on training corpus composition for polyglot text-to-speech (TTS)
Ziyao Zhang, Alessio Falai, Ariadna Sanchez, Orazio Angelini, Kayoko Yanagisawa

Simple and effective multi-sentence TTS with expressive and coherent prosody
Peter Makarov, Ammar Abbas, Mateusz Lajszczak, Arnaud Joly, Sri Karlapati, Alexis Moinet, Thomas Drugman, Penny Karanasou

Unify and conquer: How phonetic feature representation affects polyglot text-to-speech (TTS)
Ariadna Sanchez, Alessio Falai, Ziyao Zhang, Orazio Angelini, Kayoko Yanagisawa

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Senior Data Scientist - Omni-channel measurement, CMO
CA, ON, Toronto
Job summaryThe Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazons marketing teams’ decisions and thus how Amazon customers see, use, and value their experience.CMO (Campaign measurement and Optimization) team within CBA org's mission is to make Amazon’s marketing the most measurably effective in the world. Our long-term objective is to measure the incremental impact of all Amazon’s marketing investments on consumer perceptions, actions, and sales. This requires measuring Amazon’s marketing comparably and consistently across channels, business teams and countries using a comprehensive approach that integrates all Paid, Owned and Earned marketing activity. As the experts on marketing performance, we will lead the Amazon worldwide marketing community by providing critical global insights that can power marketing best practices and tenets globally.Are you passionate about Deep Learning, Causal Inference, and Big Data Systems? Interested in building new state-of-the-art measurement products at petabyte scale? Be part of a team of industry leading experts that operates one of the largest big data and machine learning stacks at Amazon. Amazon is leveraging its highly unique data and applying the latest machine learning and big data technologies to change the way marketers optimize their advertising spend. Our campaign measurement and reporting systems apply these technologies on many billions of events in near real time.You'll be one of the lead scientists tackling some of the hardest problems in advertising; measuring ads incrementality, providing estimated counterfactuals and predicting the success of advertising strategies for omni-channel campaign measurement. Working with a cross-functional team of product managers, program managers, economists and engineers you will develop state of the art causal learning, deep learning, and predictive techniques to help marketers understand the performance of their omni-channel campaigns and optimize their spends.Some things you'll do in this role:Lead full life-cycle Data Science solutions from beginning to end.Deliver with independence on challenging large-scale problems with complexity and ambiguity.Write code (Python, R, Scala, SQL, etc.) to obtain, manipulate, and analyze data.Build Machine Learning and statistical models to solve specific business problems.Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.Analyze historical data to identify trends and support optimal decision making.Apply statistical and machine learning knowledge to specific business problems and data.Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.Build decision-making models and propose effective solutions for the business problems you define.Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.Impact and Career Growth: You will invent solutions that can make billion dollar impact for Amazon as an advertiser. Define a long-term science vision for our business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.This position is based in Irvine, San Francisco, Sunnyvale, San Jose or Seattle. Key job responsibilitiesDive deep into petabyte-scale data to drive insights, identify machine-learning modeling gaps and business opportunitiesEstablish scalable, efficient, automated processes for large-scale data analysisRun regular A/B experiments, gather data, and perform statistical analysisWork with scientists, engineers and product partners to develop new machine learning approaches, and monetization strategiesConduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication
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