Amazon's 36 ICASSP papers touch on everything audio

Topics range from the predictable, such as speech recognition and noise cancellation, to singing separation and automatic video dubbing.

The International Conference on Acoustics, Speech, and Signal Processing (ICASSP) starts next week, and as Alexa principal research scientist Ariya Rastrow explained last year, it casts a wide net. The topics of the 36 Amazon research papers at this year’s ICASSP range from the classic signal-processing problems of noise and echo cancellation to such far-flung problems as separating song vocals from instrumental tracks and regulating translation length. 

A plurality of the papers, however, concentrate on the core technology of automatic speech recognition (ASR), or converting an acoustic speech signal into text:

Personalized ASR FSTs.png
To enable personalization of end-to-end automatic-speech-recognition systems, Linda Liu, Aditya Gourav and their colleagues use a word-level biasing finite state transducer, or FST (left). A subword-level FST preserves the weights of the word-level FST. For instance, the weight between state 0 and 5 of the subword-level FST (representing the word “player”) is (-1.6) +(- 1.6)+(-4.8) = -8.

Two of the papers address language (or code) switching, a more complicated version of ASR in which the speech recognizer must also determine which of several possible languages is being spoken: 

The acoustic speech signal contains more information than just the speaker’s words; how the words are said can change their meaning. Such paralinguistic signals can be useful for a voice agent trying to determine how to interpret the raw text. Two of Amazon’s ICASSP papers focus on such signals:

Several papers address other extensions of ASR, such as speaker diarization, or tracking which of several speakers issues each utterance; inverse text normalization, or converting the raw ASR output into a format useful to downstream applications; and acoustic event classification, or recognizing sounds other than human voices:

Echo:noise cancellation.png
The structure of a joint echo control and noise suppression system from Amazon. A microphone (mic) captures the output of a loudspeaker, along with noise and echo. The echo is partially cancelled by an adaptive filter f), which uses the signal to the speaker. The microphone signal then passes to a residual-echo-suppression (RES) algorithm.

Speech enhancement, or removing noise and echo from the speech signal, has been a prominent topic at ICASSP since the conference began in 1976. But more recent work on the topic — including Amazon’s two papers this year — uses deep-learning methods:

Every interaction with Alexa begins with a wake word — usually “Alexa”, but sometimes “computer” or “Echo”. So at ICASSP, Amazon usually presents work on wake word detection — or keyword spotting, as it’s more generally known:

In many spoken-language systems, the next step after ASR is natural-language understanding (NLU), or making sense of the text output from the ASR system:

In some contexts, however, it’s possible to perform both ASR and NLU with a single model, in a task known as spoken-language understanding:

SLT.png
A spoken-language-understanding system combines automatic speech recognition (ASR) and natural-language understanding (NLU) in a single model.

An interaction with a voice service, which begins with keyword spotting, ASR, and NLU, often culminates with the agent’s use of synthesized speech to relay a response. The agent’s text-to-speech model converts the textual outputs of various NLU and dialogue systems into speech:

All of the preceding research topics have implications for voice services like Alexa, but Amazon has a range of other products and services that rely on audio-signal processing. Three of Amazon’s papers at this year’s ICASSP relate to audio-video synchronization: two deal with dubbing audio in one language onto video shot in another, and one describes how to detect synchronization errors in video — as when, for example, the sound of a tennis ball being struck and the shot of the racquet hitting the ball are misaligned:

Amazon’s Text-to-Speech team has an ICASSP paper on the unusual topic of computer-assisted pronunciation training, a feature of some language learning applications. The researchers’ method would enable language learning apps to accept a wider range of word pronunciations, to score pronunciations more accurately, and to provide more reliable feedback:

Singing separation.png
The architecture of a new Amazon model for separating a recording's vocal tracks and instrumental tracks.

Another paper investigates the topic of singing voice separation, or separating vocal tracks from instrumental tracks in song recordings:

Finally, two of Amazon’s ICASSP papers, although they do evaluate applications in speech recognition and audio classification, present general machine learning methodologies that could apply to a range of problems. One paper investigates federated learning, a distributed-learning technique in which multiple servers, each with a different, local store of training data, collectively build a machine learning model without exchanging data. The other presents a new loss function for training classification models on synthetic data created by transforming real data — for instance, training a sound classification model with samples that have noise added to them artificially.

Also at ICASSP, on June 8, seven Amazon scientists will be participating in a half-hour live Q&A. Conference registrants may submit questions to the panelists online.

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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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
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