Learning Feature Representation for Automatic Speech Recognition

Feature extraction in automatic speech recognition (ASR) can be regarded as learning representations from lower-level to more abstract higher-level features. Lower-level feature can be viewed as features from the signal domain, such as perceptual linear predictive (PLP) and Mel-frequency cepstral coefficients (MFCCs) features. Higher-level feature representations can be considered as bottleneck features (BNFs) learned using deep neural networks (DNNs). In this thesis, we focus on improving feature extraction at different levels mainly for ASR. The first part of this thesis focuses on learning features from the signal domain that help ASR. Hand-crafted spectral and cepstral features such as MFCC are the main features used in most conventional ASR systems; all are inspired by physiological models of the human auditory system. However, some aspects of the signal such as pitch cannot be easily extracted from spectral features, but are found to be useful for ASR. We explore new algorithm to extract a pitch feature directly from a signal for ASR and show that this feature, appended to the other feature, gives consistent improvements in various languages, especially tonal languages. We then investigate replacing the conventional features with jointly training from the signal domain using time domain, and frequency domain approaches. The results show that our time-domain joint feature learning setup achieves state-of-the-art performance using MFCC, while our frequency domain setup outperforms them in various datasets. Joint feature extraction results in learning data or language-dependent filter banks, that can degrade the performance in unseen noise and channel conditions or other languages. To tackle this, we investigate joint universal feature learning across different languages using the proposed direct-from-signal setups. We then investigate the filter banks learned in this setup and propose a new set of features as an extension to conventional Mel filter banks. The results show consistent word error rate (WER) improvement, especially in clean condition. The second part of this thesis focuses on learning higher-level feature embedding. We investigate learning and transferring deep feature representations across different domains using multi-task learning and weight transfer approaches. They have been adopted to explicitly learn intermediate-level features that are useful for several different tasks

An Empirical Evaluation of Zero Resource Acoustic Unit Discovery

Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. AUD provides an important avenue for unsupervised acoustic model training in a zero resource setting where expert-provided linguistic knowledge and transcribed speech are unavailable. Therefore, to further facilitate zero-resource AUD process, in this paper, we demonstrate acoustic feature representations can be significantly improved by (i) performing linear discriminant analysis (LDA) in an unsupervised self-trained fashion, and (ii) leveraging resources of other languages through building a multilingual bottleneck (BN) feature extractor to give effective cross-lingual generalization. Moreover, we perform comprehensive evaluations of AUD efficacy on multiple downstream speech applications, and their correlated performance suggests that AUD evaluations are feasible using different alternative language resources when only a subset of these evaluation resources can be available in typical zero resource applications.Comment: 5 pages, 1 figure; Accepted for publication at ICASSP 201

Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation

We present an approach to reduce the performance disparity between geographic regions without degrading performance on the overall user population for ASR. A popular approach is to fine-tune the model with data from regions where the ASR model has a higher word error rate (WER). However, when the ASR model is adapted to get better performance on these high-WER regions, its parameters wander from the previous optimal values, which can lead to worse performance in other regions. In our proposed method, we utilize the elastic weight consolidation (EWC) regularization loss to identify directions in parameters space along which the ASR weights can vary to improve for high-error regions, while still maintaining performance on the speaker population overall. Our results demonstrate that EWC can reduce the word error rate (WER) in the region with highest WER by 3.2% relative while reducing the overall WER by 1.3% relative. We also evaluate the role of language and acoustic models in ASR fairness and propose a clustering algorithm to identify WER disparities based on geographic region.Comment: Accepted for publication at Interspeech 202

Prevalence of Intestinal Protozoa Infections and Associated Risk Factors among Schoolchildren in Sanandaj City, Iran

Background: Intestinal parasites are still a serious public health problem in the world, especially in developing countries. This study aimed to assess the prevalence of intestinal protozoa infections and associated risk factors among schoolchildren in Sanandaj City, Iran. Methods: This cross-sectional study involving 400 schoolchildren was carried out in 2015. Each student was selected using systematic random sampling method. Questionnaire and observation were used to identify possible risk factors. Fresh stool samples were observed using formal-ether concentration method. Results: Five species of intestinal protozoa were identified with an overall prevalence of 42.3%. No cases of helminthes infection were detected. The predominant protozoa were Blastocys hominis (21.3%) and Entamoeba coli (4.5%). Overall, 143 (35.9%) had single infections and 26 (6.4%) were infected with more than one intestinal protozoa, in which 23 (5.9%) had double intestinal protozoa infections and 3 (0.5%) had triple infections. A significant relationship was observed between intestinal protozoa infection with economic status, water resources for drinking uses, and the methods of washing vegetables (P<0.05). Conclusion: Education programs on students and their families should be implemented for the prevention and control of protozoa infections in the study area.

Osmolyte Accumulation and Sodium Compartmentation Has a Key Role in Salinity Tolerance of Pistachios Rootstocks

Physio-biochemical responses of pistachio varieties including Pistacia vera L. ‘Ghazvini’ (GH), P. vera ‘Ghermez-Pesteh’ (GP) and P. atlantica subsp. mutica (M) were assessed under salt stress to understand the common mechanisms of salt tolerance in two popular Pistacia species. In the experiment, half-sib seedlings of the varieties were subjected to high (100 mM) and severe (200 mM) levels of NaCl-induced salinity for 90 days. Growth, physiological, biochemical and ionic parameters in the roots and shoots of plants were measured in the experiment. Salinity markedly declined plant growth, and increased the number of necrotic leaves (NL) and leaf abscission. In terms of physiological responses, salinity reduced the relative water content (RWC), membrane stability index (MSI) and the concentrations of photosynthetic pigments, but increased carbohydrates and proline content in the leaves. MSI of the leaves was positively correlated with the concentrations of anthocyanins and carotenoids. Salinity increased sodium content in root and shoot tissues of the plants, and decreased potassium concentration and K/Na ratio. Among the rootstocks, GH had better performance on all parameters. Despite the high concentration of Na+ and low K/Na ratio in the shoots, the lowest number of NL was found in GH under both salinity levels. The results indicated that salt tolerance in GH was most likely related to compartmentation of Na+ ions. Finally, accumulation of osmolytes and sodium compartmentation were considered to be the most important mechanisms in the salt tolerance of pistachio rootstocks

Effective Factors in Environmental Health Status of Grocery Stores

Aims of the Study: This study was carried out to determine the effective factors in environmental health status of grocery stores in the city of Qom (located in the center of Iran). Materials & Methods: In this cross-sectional study, 283 grocery stores from 3 different regions were selected randomly using stratified sampling. Data were gathered through observation, interview, and questionnaire. The questionnaire consisted of two sections: section 1 dealt with some shop managers&rsquo; features including the age, educational level, job satisfaction, passing &ldquo;food and occupational hygiene training courses&rdquo;, store ownership, duration of employment, and features of stores including their location (Region) and environmental health condition. And section 2 dealt with the important aspects of regulations of Article 13. The data analyzed using statistical procedures such as Spearman Rank Correlation and Multivariate Regression Analysis. P-values less than 0.05 were considered as statistically significant. Results: Among the investigated factors, the manager&rsquo;s educational level had a greater impact on the environmental health conditions of grocery stores. The ownership status of grocery stores, Job satisfaction and passing &ldquo;food and occupational hygiene training courses&rdquo; were next in the ranking, respectively (p <0.001 for all measures, except for shop ownership, for which p-value was <0.02). Conclusions: Planning and implementation of effective operational and strategic programs addressing the above mentioned issues seems to be necessary. Such programs will improve the health status of the stores over time

Carbon Nanotubes Technology for Removal of Arsenic from Water

Please cite this article as: Naghizadeh A, Yari AR, Tashauoei HR, Mahdavi M, Derakhshani E, Rahimi R, Bahmani P. Carbon nanotubes technology for removal of arsenic from water. Arch Hyg Sci 2012;1(1):6-11. Aims of the Study: This study was aimed to investigate the adsorption mechanism of the arsenic removal from water by using carbon nanotubes in continuous adsorption column. Materials &amp;amp; Methods: Independent variables including carbon nanotubes dosage, contact time and breakthrough point were carried out to determine the influence of these parameters on the adsorption capacity of the arsenic from water. Results: Adsorption capacities of single wall and multiwall carbon nanotubes were about 148 mg/g and 95 mg/g respectively. The experimental data were analyzed using Langmuir and Freundlich isotherm models and equilibrium data indicate the best fit obtained with Langmuir isotherm model. Conclusions: Carbon nanotubes can be considered as a promising adsorbent for the removal of arsenic from large volume of aqueous solutions. References: 1. Lomaquahu ES, Smith AH. Feasibility of new epidemiology studies on arsenic exposures at low levels. AWWA Inorganic Contaminants Workshop. San Antonio; 1998. 2. Burkel RS, Stoll RC. Naturally occurring arsenic in sandstone aquifer water supply wells of North Eastern Wisconsin. Ground Water Monit Remediat 1999;19(2):114-21. 3. Mondal P, Majumder CB, Mohanty B. Laboratory based approaches for arsenic remediation from contaminated water: recent developments. J Hazard Mater 2006;137(1): 464-79. 4. Meenakshi RCM. Arsenic removal from water: a review. Asian J Water Environ Pollut 2006;3(1):133-9. 5. Wickramasinghe SR, Binbing H, Zimbron J, Shen Z, Karim MN. Arsenic removal by coagulation and filtration: comparison of ground waters from United States and Bangladesh. Desalination 2004;169:231-44. 6. Hossain MF. 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A keyword search system using open source software

<p>Provides an overview of a speech-to-text (STT) and keyword search (KWS) system architecture build primarily on the top of the Kaldi toolkit and expands on a few highlights. The system was developed as a part of the research efforts of the Radical team while participating in the IARPA Babel program. Our aim was to develop a general system pipeline which could be easily and rapidly deployed in any language, independently on the language script and phonological and linguistic features of the language.</p