Jump to Content
Dotan Emanuel

Dotan Emanuel

Dotan Emanuel works on the Audio biomarkers team in Google research, Tel-Aviv. Prior to this position Dotan managed the cloud network-telemetry and fault detection teams in Google cloud. Before joining Google Dotan held a leading technical position in a few Israeli startups; Genio (news personalization) which was acquired by Somoto, Discretix (mobile security) acquired by ARM, Telmap (mobile navigation) acquired by Intel, Appstream (early SaaS) & Comfy (games for young kids). Dotan Emanuel received his B.SC and M.Sc in computer science from Tel aviv university 1997 and 2003 respectively, and holds an MBA from Recanati Graduate School of Business, TAU (2008).
Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Preview abstract Computing efficient traffic signal plans is often based on the amount of traffic in an intersection, its distribution over the various intersection movements and hours as well as on performance metrics such as traffic delay. In their simple and typical form plans are fixed in the same hour over weekdays. This allows low operation costs without the necessity for traffic detection and monitoring tools. A critical factor on the potential efficiency of such plans is the similarity of traffic patterns over the days along each of the intersection movements. We refer to such similarity as the traffic stability of the intersection and define simple metrics to measure it based on traffic volume and traffic delay. In this paper, we propose an automatic probe data based method, for city-wide estimation of traffic stability. We discuss how such measures can be used for signal planning such as in selecting plan resolution or as an indication as which intersections can benefit from dynamic but expensive traffic detection tools. We also identify events of major changes in traffic characteristics of an intersection. We demonstrate the framework by using real traffic statistics to study the traffic stability in the city of Haifa along its 162 intersections. We study the impact of the time of day on the stability, detect major changes in traffic and find intersections with high and low stability. View details
    Shared computational principles for language processing in humans and deep language models
    Ariel Goldstein
    Zaid Zada
    Eliav Buchnik
    Amy Price
    Bobbi Aubrey
    Samuel A. Nastase
    Harshvardhan Gazula
    Gina Choe
    Aditi Rao
    Catherine Kim
    Colton Casto
    Lora Fanda
    Werner Doyle
    Daniel Friedman
    Patricia Dugan
    Lucia Melloni
    Roi Reichart
    Sasha Devore
    Adeen Flinker
    Liat Hasenfratz
    Omer Levy,
    Kenneth A. Norman
    Orrin Devinsky
    Uri Hasson
    Nature Neuroscience (2022)
    Preview abstract Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language. View details
    Preview abstract The ultimate goal of transfer learning is to enable learning with a small amount of data, by using a strong embedding. While significant progress has been made in the visual and language domains, the speech domain does not have such a universal method. This paper presents a new representation of speech signals based on an unsupervised triplet-loss objective, which outperforms both existing state of the art and other representations on a number of transfer learning tasks in the non-semantic speech domain. The embedding is learned on a publicly available dataset, and it is tested on a variety of low-resource downstream tasks, including personalization tasks and medical domain. The model will be publicly released. View details
    Preview abstract Automatic speech recognition (ASR) systems have dramatically improved over the last few years. ASR systems are most often trained from ‘typical’ speech, which means that underrepresented groups don’t experience the same level of improvement. In this paper, we present and evaluate finetuning techniques to improve ASR for users with non standard speech. We focus on two types of non standard speech: speech from people with amyotrophic lateral sclerosis (ALS) and accented speech. We train personalized models that achieve 62% and 35% relative WER improvement on these two groups, bringing the absolute WER for ALS speakers, on a test set of message bank phrases, to 10% for mild dysarthria and 20% for more serious dysarthria. We show that 76% of the improvement comes from only 5 min of training data. Finetuning a particular subset of layers (with many fewer parameters) often gives better results than finetuning the entire model. This is the first step towards building state of the art ASR models for dysarthric speech Index Terms: speech recognition, personalization, accessibility View details
    No Results Found