Frame-by-frame language identification in short utterances using deep neural networks

This is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, VOL 64, (2015) DOI 10.1016/j.neunet.2014.08.006This work addresses the use of deep neural networks (DNNs) in automatic language identification (LID) focused on short test utterances. Motivated by their recent success in acoustic modelling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from the short-term acoustic features. We show how DNNs are particularly suitable to perform LID in real-time applications, due to their capacity to emit a language identification posterior at each new frame of the test utterance. We then analyse different aspects of the system, such as the amount of required training data, the number of hidden layers, the relevance of contextual information and the effect of the test utterance duration. Finally, we propose several methods to combine frame-by-frame posteriors. Experiments are conducted on two different datasets: the public NIST Language Recognition Evaluation 2009 (3 s task) and a much larger corpus (of 5 million utterances) known as Google 5M LID, obtained from different Google Services. Reported results show relative improvements of DNNs versus the i-vector system of 40% in LRE09 3 second task and 76% in Google 5M LID

Methodological Thoughts from the Linguistic Field

Data are the heart and soul of any linguistic research. Regardless of how incisive an analysis might be, or how clever, it can never be any better than the data it is based upon. For the field linguist gathering data, important considerations include the selection of informants, the number of informants selection, and data collection techniques. Different research objectives, be they descriptive, prescriptive or theory-driven, require techniques appropriate to those particular goals and should be evaluated within the context of inquiry. What follows is a consideration of the techniques generally used by field linguists with a general descriptive goal within the framework of generative linguistics

Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm

Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is still immature even though there is demand from the medical and healthcare sector. The aim of this research is to assess the quality and the impact of using computerized method for early screening of speech articulation disorder among Malaysian such as the omission, substitution, addition and distortion in their speech. In this study, the statistical probabilistic approach using Hidden Markov Model (HMM) has been adopted with newly designed Malay corpus for articulation disorder case following the SAMPA and IPA guidelines. Improvement is made at the front-end processing for feature vector selection by applying the silence region calibration algorithm for start and end point detection. The classifier had also been modified significantly by incorporating Viterbi search with Genetic Algorithm (GA) to obtain high accuracy in recognition result and for lexical unit classification. The results were evaluated by following National Institute of Standards and Technology (NIST) benchmarking. Based on the test, it shows that the recognition accuracy has been improved by 30% to 40% using Genetic Algorithm technique compared with conventional technique. A new corpus had been built with verification and justification from the medical expert in this study. In conclusion, computerized method for early screening can ease human effort in tackling speech disorders and the proposed Genetic Algorithm technique has been proven to improve the recognition performance in terms of search and classification task

Adoption of ISO/TS 12913-2:2018 Protocols for Data Collection From Individuals in Soundscape Studies: an Overview of the Literature

Purpose of Review: The article reviews the literature on soundscape studies to analyse (i) which of the methods included in the Technical Specification (TS) 12913-2:2018 by the International Organization for Standardization (ISO) for collecting soundscape data from individuals are predominantly used in scientific research and (ii) what is the level of compliance with ISO recommendations of the methods employed in scientific research. // Recent Findings: The ISO/TS 12913-2:2018 provide three possible protocols for individuals’ soundscape data collection (Methods A, B, and C). Despite standardization efforts, a reference method has yet to be identified to improve comparability amongst studies and the formation of scientific evidence. // Summary: The analysis of 50 peer-reviewed papers published from 2018 (year of release of ISO/TS 12913-2) showed that Method A is the prevalent one, adopted by 94.4% of the identified studies. Full compliance with ISO technical specification recommendations is in any case quite limited, and almost no study is strictly adhering to them. Attributes are not always suitable to cover all the acoustic contexts (e.g. indoor environments). This is an indicator that the field is still developing, but it also signals that technical specification recommendations leave room for ambiguity or are not always implementable. This study is ultimately intended to offer recommendations on future development of the protocols in the standardization process

Identification of Physics Concepts in the Local Wisdom of Remo Surabaya Traditional Dance as One of the Efforts to Preserve Culture in East Java

Indonesia has a variety of cultures that can be used for ethnoscience-based learning. Indonesian culture will be better known if it is integrated into education, one of which is physics learning. One of the cultures that exist in Indonesia is the Remo Dance which is a traditional dance originating from East Java. Dance is a cultural heritage that we need to preserve because culture is a reflection of a nation. This research was conducted with the aim of identifying the physics concepts contained in the Remo Gagrak Anyar Dance so that they can be integrated into physics learning activities. This study uses a qualitative descriptive method with observations, interviews, and a literature study. Based on the results obtained, there are physics concepts in Remo Dance, including the material of muscle force, Newton's third law, gravity, circular motion, static balance, sound sources, and light. The results of this study indicate that there is potential in ethnoscience-based physics teaching materials and can be applied to physics learning to help students improve understanding and learning outcomes. Other local cultural studies can be carried out to facilitate contextual understanding of the material in various subjects and aim to preserve the culture of the Indonesian nation