Alexa’s ASRU papers concentrate on extracting high-value training data

Related data selection techniques yield benefits for both speech recognition and natural-language understanding.

This year at the IEEE Automatic Speech Recognition and Understanding (ASRU) Workshop, Alexa researchers have two papers about training machine learning systems with minimal hand-annotated data. Both papers describe automated methods for producing training data, and both describe additional algorithms for extracting just the high-value examples from that data.

Each paper, however, gravitates to a different half of the workshop’s title: one is on speech recognition, or converting an acoustic speech signal to text, and the other is on natural-language understanding, or determining a text’s meaning.

The natural-language-understanding (NLU) paper is about adding new functions to a voice agent like Alexa when training data is scarce. It involves “self-training”, in which a machine learning model trained on sparse annotated data itself labels a large body of unannotated data, which in turn is used to re-train the model.

The researchers investigate techniques for winnowing down the unannotated data, to extract examples pertinent to the new function, and then winnowing it down even further, to remove redundancies.

The automatic-speech-recognition (ASR) paper is about machine-translating annotated data from a language that Alexa already supports to produce training data for a new language. There, too, the researchers report algorithms for identifying data subsets — both before and after translation — that will yield a more-accurate model.

Three of the coauthors on the NLU paper — applied scientists Eunah Cho and Varun Kumar and applied-scientist manager Bill Campbell — are also among the five Amazon organizers of the Life-Long Learning for Spoken-Language Systems workshop, which will take place on the first day of ASRU. The workshop focuses on the problem of continuously improving deployed conversational-AI systems.

Cho and her colleagues’ main-conference paper, “Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity”, addresses an instance of that problem: teaching Alexa to recognize new “intents”.

Enlarged intents

Alexa’s NLU models classify customer requests according to domain, or the particular service that should handle a request, and intent, or the action that the customer wants executed. They also identify the slot types of the entities named in the requests, or the roles those entities play in fulfilling the request. In the request “Play ‘Undecided’ by Ella Fitzgerald”, for instance, the domain is Music and the intent PlayMusic, and the names “Undecided” and “Ella Fitzgerald” fill the slots SongName and ArtistName.

Most intents have highly specific vocabularies (even when they’re large, as in the case of the PlayMusic intent), and ideally, the training data for a new intent would be weighted toward in-vocabulary utterances. But when Alexa researchers are bootstrapping a new intent, intent-specific data is scarce. So they need to use training data extracted from more-general text corpora.

As a first pass at extracting intent-relevant data from a general corpus, Cho and her colleagues use a simple n-gram-based linear logistic regression classifier, trained on whatever annotated, intent-specific data is available. The classifier breaks every input utterance into overlapping one-word, two-word, and three-word chunks — n-grams — and assigns each chunk a score, indicating its relevance to the new intent. The relevance score for an utterance is an aggregation of the chunks’ scores, and the researchers keep only the most relevant examples.

In an initial experiment, the researchers used sparse intent-specific data to train five different machine learning models to recognize five different intents. Then they fed unlabeled examples extracted by the regression classifier to each intent recognizer. The recognizers labeled the examples, which were then used to re-train the recognizers. On average, this reduced the recognizers’ error rates by 15%.

To make this process more efficient, Cho and her colleagues trained a neural network to identify paraphrases, which are defined as pairs of utterances that have the same domain, intent, and slot labels. So “I want to listen to Adele” is a paraphrase of “Play Adele”, but “Play Seal” is not.

Augmented-data embedding
The figure above depicts embeddings of NLU training data, or geometrical representations of the data such that utterances with similar meanings are grouped together. The brown points represent annotated data specific to a new intent; the blue points represent intent-relevant data extracted from a more general data set.

The researchers wanted their paraphrase detector to be as general as possible, so they trained it on data sampled from Alexa’s full range of domains and intents. From each sample, they produced a template by substituting slot types for slot values. So, for instance, “Play Adele in the living room” became something like “Play [artist_name] in the [device_location].” From those templates, they could generate as comprehensive a set of training pairs as they wanted — paraphrases with many different sentence structures and, as negative examples, non-paraphrases with the same sentence structures.

From the data set extracted by the logistic classifier, the paraphrase detector selects a small batch of examples that offer bad paraphrases of the examples in the intent-specific data set. The idea is that bad paraphrases will help diversify the data, increasing the range of inputs the resulting model can handle.

The bad paraphrases are added to the annotated data, producing a new augmented data set, and then the process is repeated. This method halves the amount of training data required to achieve the error rate improvements the researchers found in their first experiment.

Gained in translation

The other ASRU paper, “Language Model Bootstrapping Using Neural Machine Translation for Conversational Speech Recognition”, is from applied scientist Surabhi Punjabi, senior applied scientist Harish Arsikere, and senior manager for machine learning Sri Garimella, all of the Alexa Speech group. It investigates building an ASR system in a language — in this case, Hindi — in which little annotated training data is available.

ASR systems typically have several components. One, the acoustic model, takes a speech signal as input and outputs phonetic renderings of short speech sounds. A higher-level component, the language model, encodes statistics about the probabilities of different word sequences. It can thus help distinguish between alternate interpretations of the same acoustic signal (for instance, “Pulitzer Prize” versus “pullet surprise”).

Punjabi and her colleagues investigated building a Hindi language model by automatically translating annotated English-language training data into Hindi. The first step was to train a neural-network-based English-Hindi translator. This required a large body of training data, which matched English inputs to Hindi translations.

Here the researchers ran into a problem similar to the one that Cho and her colleagues confronted. By design, the available English-Hindi training sets were drawn from a wide range of sources and covered a wide range of topics. But the annotated English data that the researchers wanted to translate was Alexa-specific.

Punjabi and her colleagues started with a limited supply of Alexa-specific annotated data in Hindi, collected through Cleo, an Alexa skill that allows multilingual customers to help train machine learning models in new languages. Using an off-the-shelf statistical model, they embedded that data, or represented each sentence as a point in a geometric space, such that sentences with similar meanings clustered together.

Then they embedded Hindi sentences extracted from a large, general, English-Hindi bilingual corpus and measured their distance from the average embedding of the Cleo data. To train their translator, they used just those sentences within a fixed distance of the average — that is, sentences whose meanings were similar to those of the Cleo data.

In one experiment, they then used self-training to fine-tune the translator. After the translator had been trained, they used it to translate a subset of the English-only Alexa-specific data. Then they used the resulting English-Hindi sentence pairs to re-train the translator.

Like all neural translators, Punjabi and her colleagues’ outputs a list of possible translations, ranked according to the translator’s confidence that they’re accurate. In another experiment, the researchers used a simple language model, trained only on the Cleo data, to re-score the lists produced by the translator according to the probability of their word sequences. Only the top-ranked translation was added to the researchers’ Hindi data set.

In another experiment, once Punjabi and her colleagues had assembled a data set of automatically translated utterances, they used the weak, Cleo-based language model to winnow it down, discarding sentences that the model deemed too improbable. With the data that was left, they built a new, much richer language model.

Punjabi and her colleagues evaluated each of these data enrichment techniques separately, so they could measure the contribution that each made to the total error rate reduction of the resulting language model. To test each language model, they integrated it into a complete ASR system, whose performance they compared to that of an ASR system that used a language model trained solely on the Cleo data.

Each modification made a significant difference in its own right. In experiments involving a Hindi data set with 200,000 utterances, re-scoring translation hypotheses, for instance, reduced the ASR system’s error rate by as much as 6.28%, model fine-tuning by as much as 6.84%. But the best-performing language model combined all the modifications, reducing the error rate by 7.86%.

When the researchers reduced the size of the Hindi data set, to simulate the situation in which training data in a new language is particularly hard to come by, the gains were even greater. At 20,000 Hindi utterances, the error rate reduction was 13.18%, at 10,000, 15.65%.

Lifelong learning

In addition to Cho, Kumar, and Campbell, the seven organizers of the Life-Long Learning for Spoken-Language Systems Workshop include Hadrian Glaude, a machine learning scientist, and senior principal scientist Dilek Hakkani-Tür, both of the Alexa AI group.

The workshop, which addresses problems of continual improvement to conversational-AI systems, features invited speakers, including Nancy Chen, a primary investigator at Singapore’s Agency for Science, Technology, and Research (A*STAR), and Alex Waibel, a professor of computer science at Carnegie Mellon University and one of the workshop organizers. The poster session includes six papers, spanning topics from question answering to emotion recognition.

Research areas

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve large-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity. Come work on the Amazon Prime Air Team! We are seeking a highly skilled weather scientist to help invent and develop new models and strategies to support Prime Air’s drone delivery program. In this role, you will develop, build, and implement novel weather solutions using your expertise in atmospheric science, data science, and software development. You will be supported by a team of world class software engineers, systems engineers, and other scientists. Your work will drive cross-functional decision-making through your excellent oral and written communication skills, define system architecture and requirements, enable the scaling of Prime Air’s operation, and produce innovative technological breakthroughs that unlock opportunities to meet our customers' evolving demands. About the team Prime air has ambitious goals to offer its service to an increasing number of customers. Enabling a lot of concurrent flights over many different locations is central to reaching more customers. To this end, the weather team is building algorithms, tools and services for the safe and efficient operation of prime air's autonomous drone fleet.