Acoustic variability and automatic recognition of children’s speech

International audienc

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Model Agglomeration for Context-Dependent Acoustic Modeling

The work describes a method for generating back-off models for context-dependent unit modeling. The main characteristic of the approach is that of building generic models by gathering statistics of detailed models, collected during Baum-Welch reestimation. The construction of back-off models does not require additional processing of the training data, allowing to quickly build different models sets with different back-off criteria starting from the same set of trained models and their statistic

Improvements In Tree-Based Language Model Representation

This paper describes an efficient way of representing a bigram language model with a finite state network used by a beam-search based and continuous speech HMM recognizer. In a previous paper [1], a compact tree-based organization of the search space was presented, that could be further reduced through an optimization algorithm. There, it was pointed out that for a 10,000-word newspaper dictation task the minimization step could have taken a lot of time and space on a standard workstation. In this paper, a new compilation technique that takes into account the particular tree-based topology is described. Results show that without additional time and space costs, the new technique produces networks equivalent to the tree-based ones but almost as small as the optimized one. 1 INTRODUCTION The most widely used Language Models (LMs) in speech recognition are n-gram models, due to both easy inference from the training corpus and easy integrability with the decoding algorithms commonly used..

Integration of Heteroscedastic Linear Discriminant Analysis (HLDA) into adaptive training

The paper investigates the integration of Heteroscedastic Linear Discriminant Analysis (HLDA) into adaptively trained speech recognizers. Two different approaches are compared: the first is a variant of CMLLR-SAT, the second is based on our previously introduced method Constrained Maximum-Likelihood Speaker Normalization (CMLSN). For the latter both HLDA projection and speaker-specific transformations for normalization are estimated w. r. t. a set of simple target-models. It is investigated if additional robustness can be achieved by estimating HLDA on normalized data. Experimental results are provided for a broadcast news task and a collection of parliamentary speeches. We show that the proposed methods lead to relative reductions in word error rate (WER) of 8% over an adapted baseline system that already includes an HLDA transform. The best performance for both tasks is achieved for the algorithm that is based on CMLSN. When compared to the combination of HLDA and CMLLR-SAT, this method leads to a considerable reduction in computational effort and to a significantly lower WER