Acoustic variability and automatic recognition of children’s speech

International audienc

Towards age-independent acoustic modeling

International audienceIn automatic speech recognition applications, due to significant differences in voice characteristics, adults and children are usually treated as two population groups, for which different acoustic models are trained. In this paper, age-independent acoustic modeling is investigated in the context of large vocabulary speech recognition. Exploiting a small amount (9 hours) of children's speech and a more significant amount (57 hours) of adult speech, age-independent acoustic models are trained using several methods for speaker adaptive acoustic modeling. Recognition results achieved using these models are compared with those achieved using age-dependent acoustic models for children and adults, respectively. Recognition experiments are performed on four Italian speech corpora, two consisting of children's speech and two of adult speech, using 64k word and 11k word trigram language models. Methods for speaker adaptive acoustic modeling prove to be effective for training age-independent acoustic models ensuring recognition results at least as good as those achieved with age-dependent acoustic models for adults and children

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

MC1R variants in childhood and adolescent melanoma: a retrospective pooled analysis of a multicentre cohort.

BACKGROUND: Germline variants in the melanocortin 1 receptor gene (MC1R) might increase the risk of childhood and adolescent melanoma, but a clear conclusion is challenging because of the low number of studies and cases. We assessed the association of MC1R variants with childhood and adolescent melanoma in a large study comparing the prevalence of MC1R variants in child or adolescent patients with melanoma to that in adult patients with melanoma and in healthy adult controls. METHODS: In this retrospective pooled analysis, we used the M-SKIP Project, the Italian Melanoma Intergroup, and other European groups (with participants from Australia, Canada, France, Greece, Italy, the Netherlands, Serbia, Spain, Sweden, Turkey, and the USA) to assemble an international multicentre cohort. We gathered phenotypic and genetic data from children or adolescents diagnosed with sporadic single-primary cutaneous melanoma at age 20 years or younger, adult patients with sporadic single-primary cutaneous melanoma diagnosed at age 35 years or older, and healthy adult individuals as controls. We calculated odds ratios (ORs) for childhood and adolescent melanoma associated with MC1R variants by multivariable logistic regression. Subgroup analysis was done for children aged 18 or younger and 14 years or younger. FINDINGS: We analysed data from 233 young patients, 932 adult patients, and 932 healthy adult controls. Children and adolescents had higher odds of carrying MC1R r variants than did adult patients (OR 1·54, 95% CI 1·02-2·33), including when analysis was restricted to patients aged 18 years or younger (1·80, 1·06-3·07). All investigated variants, except Arg160Trp, tended, to varying degrees, to have higher frequencies in young patients than in adult patients, with significantly higher frequencies found for Val60Leu (OR 1·60, 95% CI 1·05-2·44; p=0·04) and Asp294His (2·15, 1·05-4·40; p=0·04). Compared with those of healthy controls, young patients with melanoma had significantly higher frequencies of any MC1R variants. INTERPRETATION: Our pooled analysis of MC1R genetic data of young patients with melanoma showed that MC1R r variants were more prevalent in childhood and adolescent melanoma than in adult melanoma, especially in patients aged 18 years or younger. Our findings support the role of MC1R in childhood and adolescent melanoma susceptibility, with a potential clinical relevance for developing early melanoma detection and preventive strategies. FUNDING: SPD-Pilot/Project-Award-2015; AIRC-MFAG-11831

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..