Structured GMM Based on Unsupervised Clustering for Recognizing Adult and Child Speech

International audienceSpeaker variability is a well-known problem of state-of-the art Automatic Speech Recognition (ASR) systems. In particular, handling children speech is challenging because of substantial differences in pronunciation of the speech units between adult and child speakers. To build accurate ASR systems for all types of speakers Hidden Markov Models with Gaussian Mixture Densities were intensively used in combinationwith model adaptation techniques.This paper compares different ways to improve the recognition of children speech and describes a novel approach relying on Class-StructuredGaussian Mixture Model (GMM). A common solution for reducing the speaker variability relies on gender and age adaptation. First, it is proposed to replace gender and age byunsupervised clustering. Speaker classes are first used for adaptation of the conventional HMM. Second, speaker classes are used for initializing structured GMM, where the components of Gaussian densities are structured with respect to the speaker classes. In a first approach mixture weights of the structured GMM are set dependent on the speaker class. In a second approach the mixture weights are replaced by explicit dependencies between Gaussian components of mixture densities (as in stranded GMMs, but here the GMMs are class-structured).The different approaches are evaluated and compared on the TIDIGITS task. The best improvement is achieved when structured GMM is combined with feature adaptation

Photocatalytic Decomposition of Formic Acid on Mo2C-Containing Catalyst

Soluble components in the peripheral blood from experimental exposure of 14 healthy subjects to filtered air and wood smoke. Samples were collected before (pre), at 24 h and 44 h after exposure, to air and wood smoke. Data are given as medians with interquartile range. (DOCX 62 kb

TBCRC 018: phase II study of iniparib in combination with irinotecan to treat progressive triple negative breast cancer brain metastases

Nearly half of patients with advanced triple negative breast cancer (TNBC) develop brain metastases (BM) and most will also have uncontrolled extracranial disease. This study evaluated the safety and efficacy of iniparib, a small molecule anti-cancer agent that alters reactive oxygen species tumor metabolism and penetrates the blood brain barrier, with the topoisomerase I inhibitor irinotecan in patients with TNBC-BM. Eligible patients had TNBC with new or progressive BM and received irinotecan and iniparib every 3 weeks. Time to progression (TTP) was the primary end point; secondary endpoints were response rate (RR), clinical benefit rate (CBR), overall survival (OS), toxicity, and health-related quality of life. Correlative endpoints included molecular subtyping and gene expression studies on pre-treatment archival tissues, and determination of germline BRCA1/2 status. Thirty-seven patients began treatment; 34 were evaluable for efficacy. Five of 24 patients were known to carry a BRCA germline mutation (4 BRCA1, 1 BRCA2). Median TTP was 2.14 months and median OS was 7.8 months. Intracranial RR was 12 %, while intracranial CBR was 27 %. Treatment was well-tolerated; the most common grade 3/4 adverse events were neutropenia and fatigue. Grade 3/4 diarrhea was rare (3 %). Intrinsic subtyping revealed 19 of 21 tumors (79 %) were basal-like, and intracranial response was associated with high expression of proliferation-related genes. This study suggests a modest benefit of irinotecan plus iniparib in progressive TNBC-BM. More importantly, this trial design is feasible and lays the foundation for additional studies for this treatment-refractory disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-014-3039-y) contains supplementary material, which is available to authorized users