Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model

Multilingual models for Automatic Speech Recognition (ASR) are attractive as they have been shown to benefit from more training data, and better lend themselves to adaptation to under-resourced languages. However, initialisation from monolingual context-dependent models leads to an explosion of context-dependent states. Connectionist Temporal Classification (CTC) is a potential solution to this as it performs well with monophone labels. We investigate multilingual CTC in the context of adaptation and regularisation techniques that have been shown to be beneficial in more conventional contexts. The multilingual model is trained to model a universal International Phonetic Alphabet (IPA)-based phone set using the CTC loss function. Learning Hidden Unit Contribution (LHUC) is investigated to perform language adaptive training. In addition, dropout during cross-lingual adaptation is also studied and tested in order to mitigate the overfitting problem. Experiments show that the performance of the universal phoneme-based CTC system can be improved by applying LHUC and it is extensible to new phonemes during cross-lingual adaptation. Updating all the parameters shows consistent improvement on limited data. Applying dropout during adaptation can further improve the system and achieve competitive performance with Deep Neural Network / Hidden Markov Model (DNN/HMM) systems on limited data

Sticky prices in the euro area: a summary of new micro evidence

This paper presents original evidence on price setting in the euro area at the individual level. We use micro data on consumer (CPI) and producer (PPI) prices, as well as survey information. Our main findings are: (i) prices in the euro area are sticky and more so than in the US; (ii) there is evidence of heterogeneity and of asymmetries in price setting behaviour; (iii) downward price rigidity is only slightly more marked than upward price rigidity and (iv) implicit or explicit contracts and coordination failure theories are important, whereas menu or information costs are judged much less relevant by firms. --Price setting,Price stickiness,Consumer prices,Producer prices,survey data

Sticky Prices in The Euro Area: a Summary of New Micro Evidence

This paper presents original evidence on price setting in the euro area at the individual level. We use micro data on consumer (CPI) and producer (PPI) prices, as well as survey information. Our main findings are: (i) prices in the euro area are sticky and more so than in the US; (ii) there is evidence of heterogeneity and of asymmetries in price setting behaviour; (iii) downward price rigidity is only slightly more marked than upward price rigidity and (iv) implicit or explicit contracts and coordination failure theories are important, whereas menu or information costs are judged much less relevant by firms.

Sticky prices in the euro area: a summary of new micro evidence

This paper presents original evidence on price setting in the euro area at the individual level. We use micro data on consumer (CPI) and producer (PPI) prices, as well as survey information. Our main findings are: (i) prices in the euro area are sticky and more so than in the US; (ii) there is evidence of heterogeneity and of asymmetries in price setting behaviour; (iii) downward price rigidity is only slightly more marked than upward price rigidity and (iv) implicit or explicit contracts and coordination failure theories are important, whereas menu or information costs are judged much less relevant by firms. JEL Classification: C25, D40, E31consumer prices, price setting, Price stickiness, producer prices, survey data

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

Different training and adaptation techniques for multilingual Automatic Speech Recognition (ASR) are explored in the context of hybrid systems, exploiting Deep Neural Networks (DNN) and Hidden Markov Models (HMM). In multilingual DNN training, the hidden layers (possibly extracting bottleneck features) are usually shared across languages, and the output layer can either model multiple sets of language-specific senones or one single universal IPA-based multilingual senone set. Both architectures are investigated, exploiting and comparing different language adaptive training (LAT) techniques originating from successful DNN-based speaker-adaptation. More specifically, speaker adaptive training methods such as Cluster Adaptive Training (CAT) and Learning Hidden Unit Contribution (LHUC) are considered. In addition, a language adaptive output architecture for IPA-based universal DNN is also studied and tested. Experiments show that LAT improves the performance and adaptation on the top layer further improves the accuracy. By combining state-level minimum Bayes risk (sMBR) sequence training with LAT, we show that a language adaptively trained IPA-based universal DNN outperforms a monolingually sequence trained model

Using KL-divergence and multilingual information to improve ASR for under-resourced languages

Setting out from the point of view that automatic speech recognition (ASR) ought to benefit from data in languages other than the target language, we propose a novel Kullback-Leibler (KL) divergence based method that is able to exploit multilingual information in the form of universal phoneme posterior probabilities conditioned on the acoustics. We formulate a means to train a recognizer on several different languages, and subsequently recognize speech in a target language for which only a small amount of data is available. Taking the Greek SpeechDat(II) data as an example, we show that the proposed formulation is sound, and show that it is able to outperform a current state-of-the-art HMM/GMM system. We also use a hybrid Tandem-like system to further understand the source of the benefit

Boosting under-resourced speech recognizers by exploiting out of language data - Case study on Afrikaans

Under-resourced speech recognizers may benefit from data in languages other than the target language. In this paper, we boost the performance of an Afrikaans speech recognizer by using already available data from other languages. To successfully exploit available multilingual resources, we use posterior features, estimated by multilayer perceptrons that are trained on similar languages. For two different acoustic modeling techniques, Tandem and Kullback-Leibler divergence based HMMs, the proposed multilingual system yields more than 10% relative improvement compared to the corresponding monolingual systems only trained on Afrikaans

Oral rivaroxaban versus standard therapy for the treatment of symptomatic venous thromboembolism : a pooled analysis of the EINSTEIN-DVT and PE randomized studies

Background: Standard treatment for venous thromboembolism (VTE) consists of a heparin combined with vitamin K antagonists. Direct oral anticoagulants have been investigated for acute and extended treatment of symptomatic VTE; their use could avoid parenteral treatment and/or laboratory monitoring of anticoagulant effects. Methods: A prespecified pooled analysis of the EINSTEIN-DVT and EINSTEIN-PE studies compared the efficacy and safety of rivaroxaban (15 mg twice-daily for 21 days, followed by 20 mg once-daily) with standard-therapy (enoxaparin 1.0 mg/kg twice-daily and warfarin or acenocoumarol). Patients were treated for 3, 6, or 12 months and followed for suspected recurrent VTE and bleeding. The prespecified noninferiority margin was 1.75. Results: 8282 patients were enrolled. 4151 received rivaroxaban and 4131 received standard-therapy. The primary efficacy outcome occurred in 86 rivaroxaban-treated patients (2.1%) compared with 95 (2.3%) standard-therapy-treated patients (hazard ratio, 0.89; 95% confidence interval [CI], 0.66-1.19; pnoninferiority<0.001). Major bleeding was observed in 40 (1.0%) and 72 (1.7%) patients in the rivaroxaban and standard-therapy groups, respectively (hazard ratio, 0.54; 95% CI, 0.37-0.79; p=0.002). In key subgroups, including fragile patients, cancer patients, patients presenting with large clots and those with a history of recurrent VTE, the efficacy and safety of rivaroxaban was similar compared with standard-therapy. Conclusion: The single-drug approach with rivaroxaban resulted in similar efficacy to standard-therapy and was associated with a significantly lower rate of major bleeding. Efficacy and safety results were consistent among key patient subgroups

Paleoseismological and morphological evidence of slip rate variations along the North Tabriz fault (NW Iran)

International audienceNorthwest Iran is characterized by a high level of historical and instrumental seismicity related to the ongoing convergence between the Arabian and Eurasian plates. In this region, the main right-lateral strike-slip fault known as the North Tabriz fault (NTF) forms the central portion of a large crustal fault system called the Tabriz fault system (TFS). The NTF is a major seismic source along which at least three strong and destructive earthquakes have occurred since 858 AD. The two most recent destructive seismic events occurred in 1721 AD and 1780 AD, rupturing the SE and NW fault segments, respectively. This paper reports paleoseismological and quantitative geomorphologic investigations on the SE segment of the NTF, between the cities of Bostanabad and Tabriz. These observations help to improve our understanding of the seismic hazard for Tabriz city and its surrounding areas. Our field investigations revealed evidence of successive faulting events since the Late Quaternary. Paleoseismic investigations indicate that since 33.5 kyr, the SE segment of the NTF has experienced at least three major (M>7.5) seismic events, including the 1721 AD earthquake (M=7.6–7.7). Along the NW segment of the fault, however, our results suggest that the amount of strong (M~7.5) seismic events during the same period is significantly greater than along the SE segment. One possible explanation of such a difference in seismic activity is that the Late Quaternary-Holocene coseismic slip rate is decreasing along the NTF from the northwest to the southeast. This explanation contradicts the former hypothesis of a constant slip rate along the whole length of the NTF. In addition, more distributed deformation along several parallel fault branches, in a wider fault zone of the SE segment of the NTF may be considered as additional evidence for the estimation of lower rate of deformation along the fault segment. Such a slip distribution pattern can explain the existence of smaller (~300 m) Pliocene-Quaternary cumulative dextral offsets along the SE fault segment than the measured cumulative offsets along the NW segment (~800 m) of the NTF

BROADBAND BEAMPATTERN FOR MULTI-CHANNEL SPEECH ACQUISITION AND DISTANT SPEECH RECOGNITION

Spatial filtering is the fundamental characteristic of microphone array based signal acquisition which plays an important role in applications such as speech enhancement and distant speech recognition. In the array processing literature, this property is formulated upon beam-pattern steering and it is characterized for narrowband signals. This paper proposes to characterize the microphone array broadband beam-pattern based on the average output of a steered beamformer for a broadband spectrum. Relying on this characterization, we derive the directivity beam-pattern of delay-and-sum and superdirective beamformers for a linear as well as a circular microphone array. We further investigate how the broadband beam-pattern is linked to speech recognition feature extraction; hence, it can be used to evaluate distant speech recognition performance. The proposed theory is demonstrated with experiments on real data recordings