As industry-leaders in healthcare AI, we pride ourselves on the robust scientific foundation on which our platform is based on.
Corti collaborates with organizations of all sizes, researching how we can further transform the healthcare industry.
March 2022
Corti has developed an artificial intelligence (AI) that detects Out-of-Hospital Cardiac Arrest (OHCA) for emergency calls. The technical feasibility and performance of the AI was validated via several clinical studies and trials, leading us to the question of: How do you present and explain a complex AI to end-users who are operating under extreme time pressure in a high-intensity work environment?
March 2021
Fast recognition of out-of-hospital cardiac arrest (OHCA) by dispatchers might increase survival. The aim of this observational study of emergency calls was to (1) examine whether a machine learning framework (ML) can increase the proportion of calls recognizing OHCA within the first minute compared with dispatchers, (2) present the performance of ML with different false positive rate (FPR) settings, (3) examine call characteristics influencing OHCA recognition.
February 2021
The two most common paradigms for end-to-end speech recognition are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. It has been argued that the latter is better suited for learning an implicit language model. In this paper, this hypothesis is tested by measuring temporal context-sensitivity and it is evaluated how the models perform when the amount of contextual information is constrained in the audio input.
February 2021
Deep generative models have shown themselves to be state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the training distribution.
February 2021
Recent advances in self-supervised learning through contrastive training have shown that it is possible to learn a competitive speech recognition system with as little as 10 minutes of labelled data. However, these systems are computationally expensive since they require pre-training followed by fine-tuning in large parameter space. This paper explores the performance of such systems without fine-tuning by training a state-of-the-art speech recognizer on the fixed representations from the computationally demanding wav2vec 2.0 framework.
January 2021
Can a machine learning model help medical dispatchers improve recognition of out-of-hospital cardiac arrest?
May 2020
We address a challenging and practical task of labeling questions in speech in real time during telephone calls to emergency medical services in English, which embeds within a broader decision support system for emergency call-takers. We propose a novel multimodal approach to real-time sequence labelling in speech. Our model treats speech and its own textual representation as two separate modalities or views, as it jointly learns from streamed audio and its noisy transcription into text via automatic speech recognition.
May 2019
Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center.
February 2019
We introduce the Bidirectional-Inference Variational Autoencoder (BIVA), characterized by a skip-connected generative model and an inference network formed by a bidirectional stochastic inference path. We show that BIVA reaches state-of-the-art test likelihoods, generates sharp and coherent natural images, and uses the hierarchy of latent variables to capture different aspects of the data distribution.
November 2018
End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER). This suggests that predicting sequences of words directly may be helpful instead.
November 2018
Even from outside of the Tom Rossau flagship store, one is mesmerized by the warm light emanating from numerous circular shapes. Clusters of sculptural lamps populate walls and ceilings like flotillas of gently glowing jellyfish, conveying a singular sense of design drawing equally on geometry, woodcarving, and origami.
November 2018
For some time now, people have been talking about the coming AI technologies that will revolutionize society and leave no line of work unaffected. Yet, so far, disappointingly few applications have actually seen the light of day. And those who have might seem rather underwhelming in scope. Where did the future go?
October 2018
In January 2017, a morning show on San Diego’s CW6 News covered a story on how a little girl from Dallas, Texas, accidentally ordered a $300 doll house and four pounds of sugar cookies by asking the family’s Amazon Alexa if it wanted to play dollhouse. The purpose of the show was to discuss a new set of issues that consumers were facing, as these voice-based assistants had made their entry into our homes.
September 2018
We tested the effectiveness of three neural network architectures commonly used in image recognition for automatic speech recognition. These architectures: Residual Networks, Highway Networks, and Densely Connected Networks, all use nontrivial connections or skip connections. This allows networks with a very large number of layers to be trained without suffering from the vanishing gradient problem.
September 2018
We performed an exploratory study into improving end-to-end audio classification models. By introducing the intermediary regression task of approximating mel-spectrograms, we were able to classify raw waveform and mel-spectrogram input with equal accuracy. In future experiments we aim to fine-tune the end-to-end classification model to outperform models trained on hand-crafted features.
July 2018
Pre-hospital emergency care should be of the same quality for all citizens. Similarly, benefits from advances in machine learning should be spread equally across society. Unfortunately, neither is the case. Language barriers, for instance, limit the quality of pre-hospital care given to language minorities, and algorithmic bias has already led to harm of specific societal groups.
January 2018
Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instrWe have a pretty good understanding of the application of machine learning and cryptography as a security concept, but when it comes to combining the two, things become a bit nebulous and we enter fairly untraveled wilderness.
January 2018
Automatic speech recognition (ASR) is one of the most difficult tasks in natural language processing. Traditionally it has been necessary to break down the process into a series of subtasks such as speech segmentation, acoustic modelling, and language modelling. Each of these subtasks was then solved by separate, individually trained models.
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Corti has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 823383
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